With the rise of a new generation of applications (e.g., virtual and augmented reality, artificial intelligence, etc) demanding stringent performance requirements, the need for networking solutions and architectures t...
详细信息
In this study, the Directivity of the Microstrip Patch MIMO Antenna is a Novel Eight element array configuration in comparison with its performance of a Single element patch antenna. Directivity improvement and Return...
详细信息
With the explosive growth of data, hundreds of thousands of servers may be contained in a single data center. Hence, node failures are unavoidable and generally negatively effects the performance of the whole data cen...
详细信息
The intensive flow of personal data associated with the trend of computerizing aspects of people’s diversity in their daily lives is associated with issues concerning not only people protection and their trust in new...
Hundreds of thousands of Java applications have been deployed in data centers at our production Cloud to support burst peak traffic. However, detecting performance bugs in Java can be difficult as they may not prevent...
Hundreds of thousands of Java applications have been deployed in data centers at our production Cloud to support burst peak traffic. However, detecting performance bugs in Java can be difficult as they may not prevent the applications from running correctly, and may not even manifest at low loads. Profiling data collected from production provides insight into the actual running states of applications. In this paper, we aim to identify hot spots for further performance debugging by analyzing profiling data from tens of thousands of machines in the data center using a module-based approach. We present our practical experience with module classification, which allows for filtering of out-of-range modules and long-duration modules of high utilization. Our study proposes a heuristic solution to detect performance bugs in Java at scale.
Object detection algorithms constitute a pivotal task in the realm of computer vision, aiming to precisely identify and locate objects within images across diverse scenarios. In power systems, especially in aerial ima...
详细信息
ISBN:
(纸本)9798350365443
Object detection algorithms constitute a pivotal task in the realm of computer vision, aiming to precisely identify and locate objects within images across diverse scenarios. In power systems, especially in aerial images used to detect wire breaks or scattered anomalies, object detection faces significant challenges. The distribution of wires is often uneven, appearing in different sizes in high-resolution images, and the diversity of fault shapes and sizes makes it difficult for traditional object detection models to recognize accurately. Moreover, the detection of small targets also limits the precision of traditional methods. To address the issue of diversity in fault shapes and sizes in high-resolution images, which makes it difficult for traditional object detection models to recognize accurately, first, we collect fault images of wire breaks and scatter under common scenarios, constructing the 'EWF _detection' (Electric wire fault) dataset, and propose a Pyramid Multi-scale Object Detection Model, establishing the Hybrid Spatial Attention Pooling module, HSAP, which significantly enhances the multi-scale feature extraction capability. It not only improves the model's ability to focus on key features, but also suppresses unnecessary features, enhancing the expressiveness of the network, and effectively adapting to complex backgrounds, variable lighting conditions, and different line scales and directions for transmission line detection needs. This improvement makes our model perform exceptionally well in detecting rare faults in wires and accurately locate fault positions. These achievements provide reliable technical support for wire anomaly detection in the power industry and indicate a broad prospect of application in future power scenarios. Our method not only improves detection accuracy, but also maintains efficient detection speed, reducing reliance on a large number of fault line images. These advantages make our model an important technical tool for pow
Intelligence Processing Units (IPU) have proven useful for many AI applications. In this paper, we evaluate them within the emerging field of AI for simulation, where traditional numerical simulations are supported by...
详细信息
This study presents a hybrid optimization framework combining Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Multi-Objective Ant Colony Optimization (MOACO) to optimize time, cost, quality, and carbon foot...
详细信息
Artificial Intelligence techniques, such as optimization algorithms, have become essential for success in many fields. Therefore, most researchers, especially in computer and engineering sciences, focused their effort...
详细信息
Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and *** Mode Decomposition(VMD)is an adaptive and completely nonrecursive signal processing *** are two parameter...
详细信息
Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and *** Mode Decomposition(VMD)is an adaptive and completely nonrecursive signal processing *** are two parameters in VMD that have a great influence on the result of signal ***,this paper studies a signal decomposition by improving VMD based on squirrel search algorithm(SSA).It’s improved with abilities of global optimal guidance and opposition based *** original seasonal monitoring condition in SSA is *** feedback of whether the optimal solution is successfully updated is used to establish new seasonal monitoring ***-based learning is introduced to reposition the position of the population in this *** is applied to optimize the important parameters of ***-VMD model is established to remove ocular artifacts from EEG *** have verified the effectiveness of our proposal in a public dataset compared with other *** proposed method improves the SNR of the dataset from-2.03 to 2.30.
暂无评论