The primary goal of cloth simulation is to express object behavior in a realistic manner and achieve real-time performance by following the fundamental concept of *** general,the mass–spring system is applied to real...
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The primary goal of cloth simulation is to express object behavior in a realistic manner and achieve real-time performance by following the fundamental concept of *** general,the mass–spring system is applied to real-time cloth simulation with three types of ***,hard spring cloth simulation using the mass–spring system requires a small integration time-step in order to use a large stiffness ***,to obtain stable behavior,constraint enforcement is used instead of maintenance of the force of each *** force computation involves a large sparse linear solving *** to the large computation,we implement a cloth simulation using adaptive constraint activation and deactivation techniques that involve the mass-spring system and constraint enforcement method to prevent excessive elongation of *** the same time,when the length of the spring is stretched or compressed over a defined threshold,adaptive constraint activation and deactivation method deactivates the spring and generate the implicit *** method that uses a serial process of the Central Processing Unit(CPU)to solve the system in every frame cannot handle the complex structure of cloth model in *** simulation utilizes the Graphic Processing Unit(GPU)parallel processing with compute shader in OpenGL Shading Language(GLSL)to solve the system *** this paper,we design and implement parallel method for cloth simulation,and experiment on the performance and behavior comparison of the mass-spring system,constraint enforcement,and adaptive constraint activation and deactivation techniques the using GPU-based parallel method.
AI(Artificial Intelligence)workloads are proliferating in modernreal-time *** the tasks of AI workloads fluctuate over time,resourceplanning policies used for traditional fixed real-time tasks should be *** particular...
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AI(Artificial Intelligence)workloads are proliferating in modernreal-time *** the tasks of AI workloads fluctuate over time,resourceplanning policies used for traditional fixed real-time tasks should be *** particular,it is difficult to immediately handle changes inreal-time tasks without violating the deadline *** cope with thissituation,this paper analyzes the task situations of AI workloads and findsthe following two ***,resource planning for AI workloadsis a complicated search problem that requires much time for ***,although the task set of an AI workload may change over time,thepossible combinations of the task sets are known in *** on theseobservations,this paper proposes a new resource planning scheme for AIworkloads that supports the re-planning of *** of generatingresource plans on the fly,the proposed scheme pre-determines resourceplans for various combinations of ***,in any case,the workload isimmediately executed according to the resource plan ***,the proposed scheme maintains an optimized CPU(Central Processing Unit)and memory resource plan using genetic algorithms and applies it as soonas the workload *** proposed scheme is implemented in the opensourcesimulator SimRTS for the validation of its *** show that the proposed scheme reduces the energy consumptionof CPU and memory by 45.5%on average without deadline misses.
Conventional machine learning methods for software effort estimation (SEE) have seen an increase in research interest. Conversely, there are few research that try to evaluate how well deep learning techniques work in ...
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The detection of road defects is crucial for ensuring vehicular safety and facilitating the prompt repair of roadway imperfections. Existing YOLOv8-based models face the following issues: extraction capabilities and i...
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Attention deficit hyperactivity disorder (ADHD) is a type of neurodevelopmental disease affecting the mental health of children and adults. Individuals with ADHD show various symptoms such as inattention, hyperactivit...
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Attention deficit hyperactivity disorder (ADHD) is a type of neurodevelopmental disease affecting the mental health of children and adults. Individuals with ADHD show various symptoms such as inattention, hyperactivity, and impulsivity. Early diagnosis of ADHD helps to alter neural connections and refine symptoms. The clinical practice to diagnose ADHD is through subjective measures and does not significantly capture the underlying structural and functional mechanisms of the brain. Therefore, it is crucial to explore other approaches such as Artificial Intelligence (AI) to improve the accuracy and efficacy of ADHD diagnosis. Consequently, in this article we systematically investigate various Machine Learning (ML) and Deep Learning (DL) approaches as well as different diagnostic tools or modalities employed for the identification of ADHD. Particularly, a Systematic Literature Review (SLR) is conducted to review and analyze 98 selected studies published from 2021 to 2024. Subsequently, the selected studies are grouped into five categories based on the modalities utilized in these studies: physiological signals (37), magnetic resonance imaging (31), questionnaires (11), motion data (8), and others (11). We also analyze AI models which indicates that 45 studies utilized ML models, 33 studies employed DL models, and 20 studies used both. However, there are still some gaps in current research such as a lack of publicly available datasets except MRI and EEG. Although datasets for MEG and actigraphy exist, but they are underexplored and have been utilized in only a few studies. While DL models like CNNs and ANNs have been increasingly applied in recent years for ADHD diagnosis, there is a shortage of advanced DL models, including transfer learning approaches like ResNet and VGG. Additionally, there is a lack of interpretability in AI models, particularly DL models. Furthermore, most studies focus on individual modalities for ADHD diagnosis, and despite many studies showing
Due to the recent trend of software intelligence in the Fourth Industrial Revolution,deep learning has become a mainstream workload for modern computer *** the data size of deep learning increasingly grows,managing th...
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Due to the recent trend of software intelligence in the Fourth Industrial Revolution,deep learning has become a mainstream workload for modern computer *** the data size of deep learning increasingly grows,managing the limited memory capacity efficiently for deep learning workloads becomes *** this paper,we analyze memory accesses in deep learning workloads and find out some unique characteristics differentiated from traditional ***,when comparing instruction and data accesses,data access accounts for 96%–99%of total memory accesses in deep learning workloads,which is quite different from traditional ***,when comparing read and write accesses,write access dominates,accounting for 64%–80%of total memory ***,although write access makes up the majority of memory accesses,it shows a low access bias of 0.3 in the Zipf ***,in predicting re-access,recency is important in read access,but frequency provides more accurate information in write *** on these observations,we introduce a Non-Volatile Random Access Memory(NVRAM)-accelerated memory architecture for deep learning workloads,and present a new memory management policy for this *** considering the memory access characteristics of deep learning workloads,the proposed policy improves memory performance by 64.3%on average compared to the CLOCK policy.
This paper presents a parallel method for simulating real-time 3D deformable objects using the volume preservation mass-spring system method on tetrahedron *** general,the conventional mass-spring system is manipulate...
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This paper presents a parallel method for simulating real-time 3D deformable objects using the volume preservation mass-spring system method on tetrahedron *** general,the conventional mass-spring system is manipulated as a force-driven method because it is fast,simple to implement,and the parameters can be ***,the springs in traditional mass-spring system can be excessively elongated which cause severe stability and robustness issues that lead to shape restoring,simulation blow-up,and huge volume loss of the deformable *** addition,traditional method that uses a serial process of the central processing unit(CPU)to solve the system in every frame cannot handle the complex structure of deformable object in ***,the first order implicit constraint enforcement for a mass-spring model is utilized to achieve accurate visual realism of deformable objects with tough constraint *** this paper,we applied the distance constraint and volume conservation constraints for each tetrahedron element to improve the stability of deformable object simulation using the mass-spring system and behave the same as its real-world *** reduce the computational complexity while ensuring stable simulation,we applied a method that utilizes OpenGL compute shader,a part of OpenGL Shading Language(GLSL)that executes on the graphic processing unit(GPU)to solve the numerical problems *** applied the proposed methods to experimental volumetric models,and volume percentages of all objects are *** average volume percentages of all models during the simulation using the mass-spring system,distance constraint,and the volume constraint method were 68.21%,89.64%,and 98.70%,*** proposed approaches are successfully applied to improve the stability of mass-spring system and the performance comparison from our experimental tests also shows that the GPU-based method is faster than CPU-based implementation for all cases.
Recently, the increased use of artificial intelligence in healthcare has significantly changed the developments in the field of medicine. Medical centres have adopted AI applications and used it in many applications t...
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The proactive caching technique known as 'predictive caching' attempts to improve file system performance by anticipating and pre-fetching data that is likely to be requested in the future. Conventional cachin...
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Most of the research conducted in action recognition is mainly focused on general human action recognition, and most of the available datasets support studies in general human action recognition. In more specific cont...
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