Proposal of Verification System Based on Network Models for Large-Scale Network Reproduction in Virtual Environments

This is the author accepted manuscript. The final version is available from the American Physiological Society via http://dx.doi.org/10.1152/japplphysiol.01036.2015

Apparel designers and manufacturers are now using virtual garment simulation technology to evaluate 3D prototypes in virtual environments, which reduces the waste of raw materials in the sampling process. With the immediate visualization of the 3D prototypes, designers and manufacturers can communicate with each other to adjust the virtual garments seamlessly, and thereby the production process has become simplified using simulation technologies. Nevertheless, there are several limitations to the current practice. Apparel companies do not have a universal sizing standard, which leads to problems because customers need to identify their sizes in different stores, and a high return rate is expected in this case. Additionally, the psychological preferences of the wearers are not taken into account when evaluating fit. Production teams in apparel companies are only concerned with the physical fit of their targeted customer groups; they neglect the actual will of a specific customer. For example, some may like to wear oversized garments, and a just-fit size is not what they want. It is valuable to find a method to adapt the psychographic orientations of the customers to the design and production process of a garment. Therefore, we had proposed our method for developing a virtual garment fitting prediction model to predict the garment pattern parameters with anthropometric data and psychographic orientations of subjects, and previous work had proven that the prediction model has high accuracy and stability. Nevertheless, a limitation was found in that the subject data was difficult to obtain. It would be advantageous if there were more data to test in the prediction model. Thus, this study proposes the build of generative adversarial network (GAN) models to generate new body dimension data and garment parameter data. The new datasets produced by the GAN models would be favourable for an improvement in the virtual garment fitting prediction model with more training and testing data to be processed. Moreover, the synthetic datasets can be employed by designers to do research in their garment evaluation process since they have more data on similar body dimensions and preferred garment sizes to assess. A more comprehensive appraisal of the garment fit can be attained by this approach, which accelerates the design process in the apparel production stage.

Industrial applications include problems where a particularly complex motion of the mechanical system needs to be designed, analyzed and verified. Computer simulation is one of the chief means to support these goals. This paper presents a virtual reality based simulation system for design, evaluation, and verification of the complex motion of mechanical systems. The realistic visualization and immersion of the system enables an engineer to evaluate the behavior of an existing mechanical system in an intuitive and natural-like way. It is also useful in the design and development of new mechanical systems since potential mistakes can be easily found in virtual environments before actual production.

We review practical case studies of a developing method of highly reliable real-time embedded control systems using a CPU model-based hardware/software co-simulation. We take an approach that enables us to fully simulate a virtual mechanical control system including a mechatronics plant, microcontroller hardware, and object code level software. This full virtual system approach simulates control system behavior, especially that of the microcontroller hardware and software. It enables design space exploration of microarchitecture, control design validation, robustness evaluation of the system, software optimization before components design. It also avoids potential problems. The advantage of this work is that it comprises all the components in a typical control system, enabling the designers to analyze effects from different domains, for example mechanical analysis of behavior due to differences in controller microarchitecture. To further improve system design, evaluation and analysis, we implemented an integrated behavior analyzer in the development environment. This analyzer can graphically display the processor behavior during the simulation without affecting simulation results such as task level CPU load, interrupt statistics, and the software variable transition chart. It also provides useful information on the system behavior. This virtual system analysis does not require software modification, does not change the control timing, and does not require any processing power from the target microcontroller. Therefore this method is suitable for real-time embedded control system design, in particular automotive control system design that requires a high level of reliability, robustness, quality, and safety. In this study, a Renesas SH-2A microcontroller model was developed on a CoMET™platform from VaST Systems Technology. An electronic throttle control (ETC) system and an engine control system were chosen to prove this concept. The electronic throttle body (ETB) model on the Saber® simulator from Synopsys® and the engine model on MATLAB®/Simulink®simulator from MathWorks can be simulated with the SH-2A model using a newly developed co-simulation interface between MATLAB®/Simulink® and CoMET™. Though the SH-2A chip was being developed as the project was being executed, we were able to complete the OSEK OS development, control software design, and verification of the entire system using the virtual environment. After releasing a working sample chip in a later stage of the project, we found that such software could run on both actual ETC system and engine control system without critical problem. This demonstrates that our models and simulation environment are sufficiently credible and trustworthy.

Design and simulation of component-based manufacturing machine systems

The two main modeling approaches used to simulate physical motion (ANN-based and hybrid training methods) offer different means of creating simulation results with inherent trade-offs. Physics-based (PHY) models offer accuracy and interpretability, but they are also rigid and expensive to produce with respect to a complex system. Pure machine learning models, on the other hand, can provide flexible generation of predictive motion models, but they often struggle with generalization or physical consistency (making physically implausible predictions). This study presents a lightweight, data-driven framework that learns specific fundamental kinematic relationships from motion vectors that fill this gap. We design and investigate three models, 1) baseline physics-only (PHY) model, 2) pure artificial neural networks (ANN) model, and 3) a residual hybrid model that augments the PHY output with a learned correction. We demonstrate these models using a synthetic dataset of one-dimensional (1D) kinematic motion. Results indicate that all three models predict well on single-step prediction tasks; however, the models’ hybrid structure eventually shows marked improvements in stability and accuracy when rolled out across multiple steps in trajectory. Our study shows that it is possible to create a flexible, efficient framework that combines the best of both worlds: the rigidity of physics-based principles and the adaptability of dynamic machine learning models. As such, we are creating a framework that is beginning to apply across various fields, including robotics, at-home user applications, virtual environments, and interactive classroom simulations.

Image generation has emerged as a rapidly evolving field with transformative applications in entertainment, medical imaging, virtual environments, and various other industries. This paper presents a thorough analysis of technological developments and current mainstream models in image generation, focusing on Generative Adversarial Networks (GANs) and diffusion models. It explores their theoretical underpinnings, advantages, limitations, and practical applications. The study highlights that GANs are particularly effective in producing diverse and high-quality images with notable speed but are often hindered by issues such as mode collapse and training instability. In contrast, diffusion models are praised for their ability to generate high-fidelity and detailed images, though they require extensive computational resources, making them less suitable for resource-constrained environments. The paper also discusses the current challenges faced by these models, such as biases and inefficiencies, and proposes potential solutions and future research directions. By addressing these issues and exploring ways to enhance model efficiency and multi-modal generation capabilities, this study aims to provide valuable insights that will drive further innovation and practical application in the field of image generation.

The study of wireless and mobile networks is mainly based on simulations. Although simulation environments offer a convenient combination of flexibility and controllability, their largest disadvantage is that the results gained by using them are difficult to transfer into reality. This is due to the complex environment of mobile and wireless networks.In this paper we introduce a hybrid testbed approach, which consists of real mesh nodes and a virtualization environment. This combination provides on the one hand a flexible development environment for distributed network protocols and applications, and on the other hand a high degree in realism. Therefore, it allows the design and conduction of large scale networks where the results are easily transferred to the real world.

Recent advancements in computer technology have precipitated a shift towards virtual environments, accelerated by the COVID-19 pandemic. Cybercriminals have capitalized on this trend, transitioning their activities to exploit vulnerabilities in cyberspace. Malicious software (malware) has emerged as a preferred tool for launching cyber-attacks, continually evolving with sophisticated obfuscation and packing techniques to evade detection. Traditional machine learning (ML) algorithms, once effective in identifying malware, are now struggling to keep pace with these advancements. In response, deep learning (DL) algorithms offer a promising solution, leveraging their ability to discern intricate patterns and correlations within data. This study proposes a novel hybrid deep-learning-based architecture, integrating two pre-trained network models to enhance classification accuracy. Through extensive evaluation on datasets including Malimg, Microsoft BIG 2015, and Malevis, the proposed method demonstrates significant improvements in accuracy, outperforming existing ML-based malware detection methods in the literature. Specifically, the proposed method achieves an impressive accuracy of 97.78% on the Malimg dataset, underscoring its effectiveness in combating sophisticated malware variants. Keywords — Malware, malware classification, malware detection, malware variants, deep neural networks, transfer learning, deep learning.

The challenges of measuring, managing, and predicting negative side effects (i.e. cybersickness) during virtual environment (VE) interaction and unwanted aftereffects continue to persist. Most assessments have been limited to subjective questionnaires. This study investigated the effects of VE interaction on nausea (gastric activity) and the development of a quantitative model affording cybersickness prediction. This cybersickness state estimation model (CSEM) offers a quantitative (i.e. physiological based) predictability to cybersickness. The model relates time, recordable gastric activity using electrogastrogram (EGG) measures as a physiological predictor and subjective sickness estimates. This work employs neural network algorithms to model user state associated with cybersickness. Neural network models were designed, trained, and tested to determine the network trainability and accuracy. Mean SSQ total severity scores indicate significant differences between conditions. All CSEM models had a low sum of squared error. The models were found to be accurate in predicting cybersickness from unseen data.

Hardware-in-the-loop simulation for the design and verification of the control system of a series–parallel hybrid electric city-bus

The man–machine–environment (MME) of control cabins has been the very complex system in large-scale ships; it plays a very important role on the efficiency, comfort, safety of ship monitoring and operation. The virtual verification method for MME design of ship control cabins was researched. The main factors of ship control cabins MME were analyzed, and the structure of MME virtual verification method was constructed. The integrated clustering method was constructed to synthetically combine the virtual verification results based on the dominance. The virtual verification system was constructed based on EON, and the development process of virtual verification system was interpreted. Finally, a ship wheelhouse virtual verification case based on EON and virtual hall environment with applying the proposed method was used to prove the effectiveness of this method. It was expected to optimize MME design to improve the operation safety and efficiency of ships.

Network emulation system constructs a virtual network environment which has the characteristics of controllable and repeatable network conditions. This makes it possible to predict the correctness and performance of proposed new technology before deploying to Internet. In this paper we present a methodology for evaluating the correctness and performance of applications based on the PARNEM, a parallel discrete event network emulator. PARNEM employs a BSP based real-time event scheduling engine, provides flexible interactive mechanism and facilitates legacy network models reuse. PARNEM allows detailed and accurate study of application behavior. Comprehensive case studies covering bottleneck bandwidth measurement and distributed cooperative web caching system demonstrate that network emulation technology opens a wide range of new opportunities for examining the behavior of applications.

Impacts of vehicle electrification on large-scale transportation and charging infrastructure: A dynamic network modeling approach

Dendritic synaptic integration in central neurons