Deep-Learning-Based Joint Resource Scheduling Algorithms for Hybrid MEC Networks

Wang

Concurrency and Computation

et al. 2020

Traditional Chinese medicine (TCM) is found on a long‐term medical practice in China. Rare human brains can fully grasp the deep TCM knowledge derived from a tremendous amount of experience. In this big data era, a big electronic brain might be competent via deep learning techniques. For this prospect, the electronic brain needs to process various heterogeneous data, such as images, texts, audio signals, and other sensory data. It used to be a challenge to analyze the heterogeneous data by the computer‐aided system until the advances of the powerful deep learning tools. We propose a multimodal deep learning framework to mimic a TCM practitioner to diagnose a patient on the basis of multimodal perceptions of see, listen, smell, ask, and touch. The framework learns common representations from various high‐dimensional sensory data, and fuse the information for final classification. We propose to use conceptual alignment deep neural networks to embed prior knowledge and obtain interpretable latent representations. We implement a multimodal deep architecture to process tongue image and description text data for TCM diagnosis. Experiments illustrate that the multimodal deep architecture can extract effective features from heterogeneous data, produce interpretable representations, and finally achieve a higher accuracy than either corresponding unimodal architectures.

Section: Discussionmentioning

confidence: 99%

A multimodal deep architecture for traditional Chinese medicine diagnosis

Wang

Concurrency and Computation

et al. 2020

IEEE Internet Things J.

“…where F i describes that the total number of the CPU cycles of U i to be computed, D i denotes the data size transmitting to the MEC if offloading action is decided. D i and F i can be obtained by using the approaches provided in [22]. Then, one can have the execution time as…”

Section: A System Modelmentioning

confidence: 99%

“…The set of parameters for the l-th layer is θ l = {W l , b l }. σ(·) is the activation function which can be selected as sigmoid, tanh or ReLU [22]. Then the decoder with L layers describes a mapping:…”

Section: B 2r-saementioning

confidence: 99%

Stacked Autoencoder-Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks

Jiang

Wang

Dong

et al. 2020

Self Cite

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the mobile users, by optimizing offloading decision, transmission power, and resource allocation in the mobile edge computing (MEC) system. Towards this end, a deep reinforcement learning (DRL) method is proposed to obtain an online resource scheduling policy. Firstly, a related and regularized stacked auto encoder (2r-SAE) with unsupervised learning is proposed to perform data compression and representation for high dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Secondly, we present an adaptive simulated annealing based approach (ASA) as the action search method of DRL, in which an adaptive h-mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Thirdly, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. Numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks. It also shows that the proposed framework is suitable for resource scheduling problem in large-scale MEC networks, especially in the dynamic environment.Index Terms-Stacked auto encoder, deep reinforcement learning, adaptive simulated annealing, mobile edge computing.

“…The basic elements in HTM are miniature columns where each is made of several neural cells, and spans multiple cellular layers in the neocortex. Unlike traditional neural networks and other artificial neural networks, i.e., DNN [36], CNN, and RNN, that model the neuron as computing a single weighted sum of all or part of its synapses, a neuron in the HTM model is modelled as the neocortical pyramidal neuron, which has thousands of excitatory synapses located on dendrites. There are three sources of input to the neuron cell, including feedforward, context, and feedback.…”

Section: Htm-based Sequence Learning Network For Gait Semantic Featurmentioning

confidence: 99%

Gait Recognition and Understanding Based on Hierarchical Temporal Memory Using 3D Gait Semantic Folding

Luo

Tjahjadi

2020

Sensors

Gait recognition and understanding systems have shown a wide-ranging application prospect. However, their use of unstructured data from image and video has affected their performance, e.g., they are easily influenced by multi-views, occlusion, clothes, and object carrying conditions. This paper addresses these problems using a realistic 3-dimensional (3D) human structural data and sequential pattern learning framework with top-down attention modulating mechanism based on Hierarchical Temporal Memory (HTM). First, an accurate 2-dimensional (2D) to 3D human body pose and shape semantic parameters estimation method is proposed, which exploits the advantages of an instance-level body parsing model and a virtual dressing method. Second, by using gait semantic folding, the estimated body parameters are encoded using a sparse 2D matrix to construct the structural gait semantic image. In order to achieve time-based gait recognition, an HTM Network is constructed to obtain the sequence-level gait sparse distribution representations (SL-GSDRs). A top-down attention mechanism is introduced to deal with various conditions including multi-views by refining the SL-GSDRs, according to prior knowledge. The proposed gait learning model not only aids gait recognition tasks to overcome the difficulties in real application scenarios but also provides the structured gait semantic images for visual cognition. Experimental analyses on CMU MoBo, CASIA B, TUM-IITKGP, and KY4D datasets show a significant performance gain in terms of accuracy and robustness.