Hierarchical Power Management Architecture and Optimal Local Control Policy for Energy Efficient Networks

Wei, Yifei,Wang, Xiaojun,Fialho, Leonardo,Bruschi, Roberto,Ormond, Olga,Collier, Martin The Korea Institute of Information and Commucation 2016 Journal of communications and networks Vol.18 No.4

Since energy efficiency has become a significant concern for network infrastructure, next-generation network devices are expected to have embedded advanced power management capabilities. However, how to effectively exploit the green capabilities is still a big challenge, especially given the high heterogeneity of devices and their internal architectures. In this paper, we introduce a hierarchical power management architecture (HPMA) which represents physical components whose power can be monitored and controlled at various levels of a device as entities. We use energy aware state (EAS) as the power management setting mode of each device entity. The power policy controller is capable of getting information on how many EASes of the entity are manageable inside a device, and setting a certain EAS configuration for the entity. We propose the optimal local control policy which aims to minimize the router power consumption while meeting the performance constraints. A first-order Markov chain is used to model the statistical features of the network traffic load. The dynamic EAS configuration problem is formulated as a Markov decision process and solved using a dynamic programming algorithm. In addition, we demonstrate a reference implementation of the HPMA and EAS concept in a NetFPGA frequency scaled router which has the ability of toggling among five operating frequency options and/or turning off unused Ethernet ports.

Hierarchical Power Management Architecture and Optimal Local Control Policy for Energy Efficient Networks

Yifei Wei,Xiao-jun Wang,Leonardo Fialho,Roberto Bruschi,Olga Ormond,Martin Collier 한국통신학회 2016 Journal of communications and networks Vol.18 No.4

Since energy efficiency has become a significant concernfor network infrastructure, next-generation network devices areexpected to have embedded advanced power management capabilities. However, how to effectively exploit the green capabilities isstill a big challenge, especially given the high heterogeneity of devicesand their internal architectures. In this paper, we introduce ahierarchical power management architecture (HPMA) which representsphysical components whose power can be monitored andcontrolled at various levels of a device as entities. We use energyaware state (EAS) as the power management setting mode of eachdevice entity. The power policy controller is capable of getting informationon how many EASes of the entity are manageable insidea device, and setting a certain EAS configuration for the entity. Wepropose the optimal local control policy which aims to minimizethe router power consumption while meeting the performance constraints. A first-order Markov chain is used to model the statisticalfeatures of the network traffic load. The dynamic EAS configurationproblemis formulated as aMarkov decision process and solvedusing a dynamic programming algorithm. In addition, we demonstratea reference implementation of the HPMA and EAS conceptin a NetFPGA frequency scaled router which has the ability of togglingamong five operating frequency options and/or turning offunused Ethernet ports.

Energy Efficient Topology Control for Multi-Hop Relay Cellular Networks Based on Flow Management

Yifei Wei,Yongfu Hou,Li Li,Mei Song 한국통신학회 2017 Journal of communications and networks Vol.19 No.6

The rapid expansion of mobile communication networkshas led to a significant increase of energy consumption, and the energyconsumption of wireless access networks accounted for nearly90%. As an important form of the wireless access networks, multihoprelay networks have been widely deployed in the existing cellularnetworks. Consequently, improving the energy efficient ofmulti-hop relay cellular networks is crucial for communication networks. In this paper, we design an energy flow management frameworkfor multi-hop relay cellular networks, which contains routingplanning, traffic adjustment and power allocation. In orderto reduce the energy consumption and guarantee the system capacity,a heuristic algorithm is designed in this paper, which mixesthe ant colony algorithm with convex optimization theory. In orderto verify the effect of the proposed algorithm, we make numericalsimulation and compare it with other network management strategies,such as ant colony algorithm, shortest path routing strategyand greedy algorithm. Simulation results show that by our algorithm,the energy consumption can be reduced compared with theant colony algorithm, shortest path and greedy algorithm while ensuringthe demand of quality of service (QoS).

A Deep Belief Network for Electricity Utilisation Feature Analysis of Air Conditioners Using a Smart IoT Platform

( Wei Song ),( Ning Feng ),( Yifei Tian ),( Simon Fong ),( Kyungeun Cho ) 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.1

Currently, electricity consumption and feedback mechanisms are being widely researched in Internet of Things (IoT) areas to realise power consumption monitoring and management through the remote control of appliances. This paper aims to develop a smart electricity utilisation IoT platform with a deep belief network for electricity utilisation feature modelling. In the end node of electricity utilisation, a smart monitoring and control module is developed for automatically operating air conditioners with a gateway, which connects and controls the appliances through an embedded ZigBee solution. To collect electricity consumption data, a programmable smart IoT gateway is developed to connect an IoT cloud server of smart electricity utilisation via the Internet and report the operational parameters and working states. The cloud platform manages the behaviour planning functions of the energy-saving strategies based on the power consumption features analysed by a deep belief network algorithm, which enables the automatic classification of the electricity utilisation situation. Besides increasing the user’s comfort and improving the user’s experience, the established feature models provide reliable information and effective control suggestions for power reduction by refining the air conditioner operation habits of each house. In addition, several data visualisation technologies are utilised to present the power consumption datasets intuitively.

Investigation of Different Conduction States on the Performance of NMOS-Based Power Clamp ESD Device

Wei Weipeng,Wang Yang,Chen Xijun,Zheng Yifei,Li Jieyu,Cao Pei,Cao Wenmiao 대한전기학회 2021 Journal of Electrical Engineering & Technology Vol.16 No.3

This article investigates the eff ects of diff erent gate coupling voltage and gate voltage duration on electro-static discharge (ESD) performance of several NMOS-based power rail protection devices. Through simulation and transmission line pulse (TLP) test, it is found that there are two modes in the conduction process of the main clamping NMOS: channel conduction state and parasitic NPN conduction state. Diff erent gate voltage and duration bring the two conduction states diff erent proportions in the whole working process, which give the device very diff erent robustness. The results show that under the condition of small gate voltage and long duration and the condition of large gate voltage and short duration, the device can achieve optimal performance because the trigger voltage can be reduced, and the parasitic NPN can be turned on in time to release most of the current

Pointwise CNN for 3D Object Classification on Point Cloud

( Wei Song ),( Zishu Liu ),( Yifei Tian ),( Simon Fong ) 한국정보처리학회 2021 Journal of information processing systems Vol.17 No.4

Three-dimensional (3D) object classification tasks using point clouds are widely used in 3D modeling, face recognition, and robotic missions. However, processing raw point clouds directly is problematic for a traditional convolutional network due to the irregular data format of point clouds. This paper proposes a pointwise convolution neural network (CNN) structure that can process point cloud data directly without preprocessing. First, a 2D convolutional layer is introduced to percept coordinate information of each point. Then, multiple 2D convolutional layers and a global max pooling layer are applied to extract global features. Finally, based on the extracted features, fully connected layers predict the class labels of objects. We evaluated the proposed pointwise CNN structure on the ModelNet10 dataset. The proposed structure obtained higher accuracy compared to the existing methods. Experiments using the ModelNet10 dataset also prove that the difference in the point number of point clouds does not significantly influence on the proposed pointwise CNN structure.

A CPU-GPU Hybrid System of Environment Perception and 3D Terrain Reconstruction for Unmanned Ground Vehicle

Wei Song,Shuanghui Zou,Yifei Tian,Su Sun,Simon Fong,조경은,Lvyang Qiu 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.6

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmannedground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surroundingterrain reconstruction crucially influences decision making in UGVs. To increase the processing speed ofenvironment information analysis, we develop a CPU-GPU hybrid system of automatic environmentperception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists ofthree functional modules, namely, multi-sensor data collection and pre-processing, environment perception,and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processingfunction registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motioninformation into a global terrain model after filtering redundant and noise data according to the redundancyremoval principle. In the environment perception module, the registered discrete points are clustered intoground surface and individual objects by using a ground segmentation method and a connected componentlabeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversedand obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibratesthe projection matrix between the mounted LiDAR and cameras to map the local point clouds onto thecaptured video images. Texture meshes and color particle models are used to reconstruct the ground surfaceand objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallelcomputation method to implement the applied computer graphics and image processing algorithms in parallel.

A CPU-GPU Hybrid System of Environment Perception and 3D Terrain Reconstruction for Unmanned Ground Vehicle

Song, Wei,Zou, Shuanghui,Tian, Yifei,Sun, Su,Fong, Simon,Cho, Kyungeun,Qiu, Lvyang Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.6

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.

A Deep Belief Network for Electricity Utilisation Feature Analysis of Air Conditioners Using a Smart IoT Platform

Song, Wei,Feng, Ning,Tian, Yifei,Fong, Simon,Cho, Kyungeun Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.1

Currently, electricity consumption and feedback mechanisms are being widely researched in Internet of Things (IoT) areas to realise power consumption monitoring and management through the remote control of appliances. This paper aims to develop a smart electricity utilisation IoT platform with a deep belief network for electricity utilisation feature modelling. In the end node of electricity utilisation, a smart monitoring and control module is developed for automatically operating air conditioners with a gateway, which connects and controls the appliances through an embedded ZigBee solution. To collect electricity consumption data, a programmable smart IoT gateway is developed to connect an IoT cloud server of smart electricity utilisation via the Internet and report the operational parameters and working states. The cloud platform manages the behaviour planning functions of the energy-saving strategies based on the power consumption features analysed by a deep belief network algorithm, which enables the automatic classification of the electricity utilisation situation. Besides increasing the user's comfort and improving the user's experience, the established feature models provide reliable information and effective control suggestions for power reduction by refining the air conditioner operation habits of each house. In addition, several data visualisation technologies are utilised to present the power consumption datasets intuitively.

Exploring the Cocktail Factor Approach to Generate Salivary Gland Progenitors through Co-Culture Techniques

Zhang Yifei,Yan Shuang,Mei Zi,Zhang He,Ding Chong,Zhang Siqi,Wei Shicheng 한국조직공학과 재생의학회 2024 조직공학과 재생의학 Vol.21 No.5

Background: The derivation of salivary gland (SG) progenitors from pluripotent stem cells (PSCs) presents significant potential for developmental biology and regenerative medicine. However, the existing protocols for inducing SG include limited factors, making it challenging to mimic the in vivo microenvironment of embryonic SGs. Methods: We reported a cocktail factor approach to promote the differentiation of mouse embryonic stem cell (mESC)-derived oral epithelium (OE) into SG progenitors through a three-dimensional co-culture method. Upon confirming that the embryonic SG can promote the differentiation of mESC-derived OE, we performed RNA sequence analysis to identify factors involved in the differentiation of SG progenitors. Results: Our findings highlight several efficient pathways related to SG development, with frequent appearances of four factors: IFN-γ, TGF-β2, EGF, and IGF-1. The combined treatment using these cocktail factors increased the expression of key SG progenitor markers, including Sox9, Sox10, Krt5, and Krt14. However, absence of any one of these cocktail factors did not facilitate differentiation. Notably, aggregates treated with the cocktail factor formed SG epithelial-like structures and pre-bud-like structures on the surface. Conclusion: In conclusion, this study offers a novel approach to developing a differentiation protocol that closely mimics the in vivo microenvironment of embryonic SGs. This provides a foundation for generating PSC-derived organoids with near-physiological cell behaviors and structures. Background: The derivation of salivary gland (SG) progenitors from pluripotent stem cells (PSCs) presents significant potential for developmental biology and regenerative medicine. However, the existing protocols for inducing SG include limited factors, making it challenging to mimic the in vivo microenvironment of embryonic SGs. Methods: We reported a cocktail factor approach to promote the differentiation of mouse embryonic stem cell (mESC)-derived oral epithelium (OE) into SG progenitors through a three-dimensional co-culture method. Upon confirming that the embryonic SG can promote the differentiation of mESC-derived OE, we performed RNA sequence analysis to identify factors involved in the differentiation of SG progenitors. Results: Our findings highlight several efficient pathways related to SG development, with frequent appearances of four factors: IFN-γ, TGF-β2, EGF, and IGF-1. The combined treatment using these cocktail factors increased the expression of key SG progenitor markers, including Sox9, Sox10, Krt5, and Krt14. However, absence of any one of these cocktail factors did not facilitate differentiation. Notably, aggregates treated with the cocktail factor formed SG epithelial-like structures and pre-bud-like structures on the surface. Conclusion: In conclusion, this study offers a novel approach to developing a differentiation protocol that closely mimics the in vivo microenvironment of embryonic SGs. This provides a foundation for generating PSC-derived organoids with near-physiological cell behaviors and structures.