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检索条件"任意字段=2022 International Conference on Algorithms, Microchips and Network Applications"

共 1155 条记录，以下是41-50 订阅

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Research on infrared optical CO detection based on BP neural network algorithm 3

Research on infrared optical CO detection based on BP neural...

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3rd international conference on algorithms, microchips, and network applications, AMNA 2024

作者： Wang, Xiaodong Shi, Xian Duan, Hu Zhou, Laisan Wu, Hexiang Sinopec Shengli Oilfield Branch Technical Testing Center Dongying257000 China School of Mechanical and Electrical Engineering Qingdao University of Science and Technology Qingdao266061 China

ISBN: (纸本)9781510680098

The article takes the prevention of accidents caused by CO in daily life as the background and introduces a method of CO concentration detection parameter modulation and system denoising. The system adopts a hybrid detection technology of tunable semiconductor laser absorption spectroscopy (TDLAS) and wavelength modulation (WMS). It is achieving effective suppression of background gases in the environment. The system is combined with a BP neural network to invert the concentration of CO in the gas. Finally, the system noise is processed through wavelet transform to achieve the completion and optimization of the smoke detection algorithm. This has high practical value and guiding significance for the combination of TDLAS CO detection simulation system and hardware system of WOA-BP neural network. © 2024 SPIE.

关键词： Wavelet transforms

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The developments and applications of brain-like computing chips

The developments and applications of brain-like computing ch...

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2022 international conference on algorithms, microchips and network applications

作者： Nie, Yuzhou Ren, Tong Shi, Zhe School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China College of Artificial Intelligence Nanjing Agricultural University Nanjing211800 China Maynooth International Engineering College Fuzhou University Fuzhou350108 China

ISBN: (纸本)9781510653290

In recent years, with the development of artificial intelligence chip, brain-like computing chip has become one of the hotspots in the field of artificial intelligence chip. Brain-like computing chip is a micro nano photoelectric device that can simulate the functions of neurons and synapses by physical and physiological deconstruction of human brain or animal brain. Brain-like chip is the basis of other high-level artificial intelligence tasks. Because of its excellent performance and the ability to subvert the "memory wall"problem existing in von Neumann architecture, it has extremely important research significance in the fields of artificial intelligence, computer science and so on. With the increasing need of the hardware hashrate and the invention of advanced methods, all kinds of brain-like smart chips have developed rapidly. Thus, This paper aims to evaluate the developments and applications of brain-like computing chips with a more thorough view. First, the basic structure and development of class chip is described;Second, the principles, methods, process, structures and overall frameworks are introduced in detail based on different brain-like computing chip models, such as Loihi, TrueNorth, and "Darwin NPU 2". Besides, experiments are carried out according to the Tianjic chip model. The accuracy, memory, time-consuming and other data of Tianjic chip ("Tianjic â. "and "Tianjic â. ") and original model tested with full connected ANNs and small-scale CNNs are analyzed and compared, trying to figure out Complete and accurate deficiencies and advantages of the Tianjic chip. Finally, combined with the current research progress of brain-like computing chip, the obstacles existing in the research of it are summarized, and the future research trend and main goals of brain-like computing chip technology are prospected. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Brain

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Ensemble MagNet:A Fast Training network Against Adversarial Examples 2

Ensemble MagNet:A Fast Training Network Against Adversarial ...

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2nd international conference on algorithms, microchips, and network applications, AMNA 2023

作者： Zhuang, Zebin Guangzhou University China

ISBN: (纸本)9781510664791

In recent years, deep learning has excellent applications in many fields, especially in computer vision. However, it has been found that deep learning systems are vulnerable to subtle interference, which is almost imperceptible to humans. This leads to model decision errors and potentially disastrous consequences. Therefore, AI scientists have carried out many researches on this, and MagNet, Feature Squeezing and Defense-GAN are excellent research achievements. In this paper, we propose to improve MagNet. By proposing a new network structure and integrating ensemble learning technology, experiments show that this MagNet combined with ensemble learning technology has many advantages over the original MagNet. From the perspective of training time, the training time required by MagNet based on ensemble learning is reduced by half compared with the original MagNet. From the perspective of defense capability, Under the condition that the training time is sufficient and long, there is little difference between their defense capabilities. Under the condition that the training time is limited, MagNet based on ensemble learning is much better than the original MagNet. Similarly, according to this experimental idea, this paper conducts comparative experiments with Feature Squeezing algorithm and Defense-GAN algorithm from the perspective of training time and defense capability, and finds that MagNet based on ensemble learning also has advantages. © 2023 SPIE.

关键词： Image classification

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Passive traffic analysis based on resource occupancy of mobile communication uplink control channel 3

Passive traffic analysis based on resource occupancy of mobi...

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3rd international conference on algorithms, microchips, and network applications, AMNA 2024

作者： Zhang, Hang Zhuang, Liqi Wei, Dong Huang, Weiqing Li, Jing School of Cyber Security University of Chinese Academy of Sciences Beijing 100049 ChinaInstitute of Information Engineering Chinese Academy of Sciences Beijing 100093 China

ISBN: (纸本)9781510680098

Traffic identification is a vital technology in network security. Currently, the identification of mobile network traffic is based on the downlink data in the air interface. This is because it is difficult to synchronize uplinks and obtain uplink traffic data in real-world environments. We propose to utilize mobile communication network sideband resource occupancy for traffic identification. This method captures the uplink IQ data and draws a time-frequency resource map. In order to reduce the computational complexity, we only use the sideband portion of the time-frequency resource map for identification. Based on the different colors reflected on the time-frequency resource map by different users' uplink transmitting power, we distinguish the number of users by color and separate the different user data. The result shows that the accuracy of user number identification is up to 95%. Finally, we use Resnet18 to identify the service of the separated pictures. The F1 parameter of the Resnet18 network reaches 88%. © 2024 SPIE.

关键词： network security

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Narrow-band and Low Latency Routing algorithms in Wireless Mesh networks for Internet of Things applications 2

Narrow-band and Low Latency Routing Algorithms in Wireless M...

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2nd international conference on algorithms, microchips, and network applications, AMNA 2023

作者： He, Hailiang Tan, Ronggang Huang, Xiufeng Shenzhen ORVIBO Technology Co. Ltd China

ISBN: (纸本)9781510664791

With the development of smart homes, more and more smart home devices are equipped with networking features, and networking technology has been widely applied in smart *** order to meet the networking requirements of small, low power consumption, low cost and short distance portable terminal equipment, the short distance wireless technology has been developed rapidly[1]. Currently, the short distance wireless networking technologies used in smart homes in the field of whole-house intelligence mainly include star wireless network technologies, represented by WiFi and bluetooth, and wireless mesh network technologies, represented by Bluetooth (BLE) mesh and ZigBee[2]. Compared to the star wireless network technologies such as WiFi and Bluetooth, the wireless mesh network technologies have the advantage that the device itself can act as a route node for other devices,the communication range is extended by means of multiple hops[3] , then all the nodes can constitute a mesh so that the communication signals can well cover the entire house, avoiding the problem of weak signals or connection failures incurred by the fact that the WiFi device is located in remote corners. However, the BLE mesh and ZigBee wireless mesh network technologies still have limitations. BLE mesh networks use the flooding algorithm to transmit data from the source node to the destination node from near to far layer by layer, which is like flooding. But due to the use of the flooding routing algorithm, all devices need to perform broadcast forwarding in each communication process, resulting in low communication efficiency and high wireless bandwidth occupancy. ZigBee uses a wireless routing algorithm based on an Ad hoc On-Demand Distance Vector (AODV) standard, which is more efficient than BLE mesh, but it is still a bit inadequate in the case of multi-node networks, reflected in not ideal stability and communication delay. In this paper, the O-Mesh mesh routing algorithm with low network ove

关键词： Routing algorithms

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Neural network-based comfort evaluation method for handheld sports products 2

Neural network-based comfort evaluation method for handheld ...

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2nd international conference on algorithms, microchips, and network applications, AMNA 2023

作者： Tang, Xiaoyu Zhang, Ruiqiu South China University of Technology Guangzhou China

ISBN: (纸本)9781510664791

In order to provide a set of quantitative evaluation of the comfort of hand-held sports products, this study preliminarily constructed the comfort evaluation index system of hand-held sports products from the perspectives of performance, tactile characteristics, quality and appearance, physical stimulation, discomfort, morphological characteristics, user demographic characteristics and hand characteristics. Secondly, 165 groups of data were collected through the experiment, and factor analysis was used to reduce the dimension of the original index. Then neural network was used to establish the mapping relationship between the evaluation index and the comfort evaluation, and the evaluation model of the comfort of hand-held sports products was obtained. The results show that the relative error between the predicted value and the actual value of the network model is basically less than 5% in the practical application of the evaluation method taking the badminton racket as an example, which reflects that the comfort evaluation method can effectively reduce the correlation between the original indexes, achieve the accurate evaluation of the comfort of products, and verify the effectiveness and feasibility of the evaluation method. The combination of neural network and factor analysis can quantitatively analyze and evaluate the comfort of hand-held sports products, and provide reliable suggestions for manufacturers to design and produce more comfortable products in the form of data. © 2023 SPIE.

关键词： Sports

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Optimization method for rapid emergency recovery of power failure in distribution network based on multi-agent algorithm 3

Optimization method for rapid emergency recovery of power fa...

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3rd international conference on algorithms, microchips, and network applications, AMNA 2024

作者： Yang, Jingsi Yi, Rong Fu, Yao Wang, Zhaojing Li, Tongqing Wang, Junxi Economic and Technological Research Institute of State Grid Heilongjiang Electric Power Co. Ltd. Heilongjiang Harbin150010 China

ISBN: (纸本)9781510680098

Power failure in distribution network affects the quality of people's daily electricity consumption, so it is of great practical significance to quickly locate the fault location and realize power supply recovery for improving the reliability and safety of power supply. Based on this, this paper proposes an optimization method for rapid emergency recovery of distribution network power failure based on multi-agent algorithm. In the single power supply mode, the switching function is designed, and the power failure location result of distribution network based on multi-agent algorithm is determined by analyzing the power generation efficiency function of equipment. Automatic data is used to optimize the rapid emergency recovery of power failure, and multi-agent algorithm is introduced into it to realize the optimization method of rapid emergency recovery of power failure in distribution network. The experimental results show that the precision, recall and F1 score of the research method are all above 95%, and the power generation effect of the equipment is high, which can restore the normal operation of the distribution network more quickly and effectively. © 2024 SPIE.

关键词： Recovery

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BP neural network based wireless sensor network for solar energy prediction 2

BP neural network based wireless sensor network for solar en...

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2nd international conference on algorithms, microchips, and network applications, AMNA 2023

作者： Duan, Hongyi Zhang, Jianan Peng, Haohui Faculty of Electronic and Information Engineering Xi'an Jiaotong University Xianning road 710049 China Shanghai University of Finance and Economics University Yangpu310110 China The School of Astronomy and Space Science Nanjing University Xianlin Avenue Qixia210033 China

ISBN: (纸本)9781510664791

Wireless sensor networks are generally deployed in remote areas and areas with complex geographical environments. It is difficult to replace sensor node batteries. Energy acquisition sensor network nodes are powered by solar cells to provide energy to wireless sensor nodes. The randomness and uncertainty of solar energy in the monitoring area make it impossible to continuously provide energy for sensor network nodes. By predicting the results of solar energy and combining the information collected by the wireless sensor network, the energy usage of wireless sensor network nodes is reasonably planned, thereby improving wireless transmission. sensor network lifetime and the accuracy and reliability of sensor node measurement information. Prediction of solar energy in the monitoring area is an important part of improving the monitoring quality and life of wireless sensor networks. In this paper, the BP neural network combined with the climatic factors in the wireless sensor network monitoring area, such as illumination, the average diffuse reflection intensity of the solar panel on the day, etc., are used as reference data to predict the solar energy in the wireless sensor network monitoring area. Compared with the traditional exponentially weighted moving average algorithm (EWMA), the error rate of the prediction results is lower and the prediction effect is better due to the comprehensive consideration of various climatic factors. © 2023 SPIE.

关键词： Solar energy

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Towards a container scheduling policy for alleviating total startup latency in serverless computing platform 3

Towards a container scheduling policy for alleviating total ...

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3rd international conference on algorithms, microchips, and network applications, AMNA 2024

作者： Wang, Chang Liu, Zhiqiong Liu, Jin Li, Wang Chen, Junxin Cloud Network Operation Technology Research Institute Research Institute of China Telecom Co. Ltd. Guangzhou510630 China

ISBN: (纸本)9781510680098

FaaS enables users to focus on developing function codes rather than managing complex infrastructure, as the serverless computing platform takes responsibility for resource management and dynamically scales computing resources for serverless functions. While serverless computing platform provides efficient hardware resource management and provisioning, they suffer from weaker computing performance due to the latency associated with serverless function startup. Startup latency refers to the time required to prepare execution environments for user functions. To alleviate this latency, this paper proposes a container scheduling policy aimed at reducing startup latency by reducing the likelihood of container cold starts. This is achieved by unifying language runtime images, creating pre-warm container pools, and warm containers. We formulate the startup latency problem and implement a scheduling policy in a serverless computing platform. Simulation results demonstrate that our proposed scheduling policy effectively reduces overall startup latency while ensuring optimal computing performance for user functions. © 2024 SPIE.

关键词： Containers

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network communication optimization of RCCL communication library in Multi-NIC systems 3

Network communication optimization of RCCL communication lib...

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3rd international conference on algorithms, microchips, and network applications, AMNA 2024

作者： He, Shuaiming Wan, Wei Li, Junhong School of Computer and Artificial Intelligence Zhengzhou University Henan Zhengzhou450001 China Co. Ltd. Beijing100193 China Tsinghua University Software School Beijing100084 China

ISBN: (纸本)9781510680098

With the widespread application of deep learning frameworks, large-scale computing and GPU programming are receiving increased attention. For upper-layer applications that utilize GPUs for computational communication, such as TensorFlow and PyTorch, improving the communication efficiency of the underlying communication library is of paramount importance to enhance the overall performance of the frameworks. Among them, the RCCL (Rocm Collective Communication Library) GPU communication library, provided by the Rocm (Radeon Open Compute platform) computing platform, supports various collective communication operations and point-to-point operations. Through analysis, we have identified a problem in the initialization and usage of the ring channel network in the RCCL library, specifically in multi-network card systems. This issue results in certain network cards being unable to communicate, leading to wasted system resources. To address this problem, optimizations can be made at the code level by introducing data structures and algorithms to control the invocation of network cards. The goal is to adjust the usage strategy of multiple network cards in the ring channel network without modifying the original design concept of RCCL. After optimization, extensive evaluations were conducted using a large-scale GPU cluster. The optimized RCCL library achieved significant improvements in communication performance. Under a communication scale of 16 compute nodes and 64 GPUs, the peak bandwidth increased from 5.28GB/s to 7.78GB/s. In inter-node collective communication tests, the performance improvement reached up to 60%. The improved RCCL library provides better low-level communication performance for upper-layer applications on the Rocm computing platform, offering enhanced communication support. © 2024 SPIE.

关键词： Graphics processing unit

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