scholarly journals Exploring UAV's multi-domain joint anti-jamming intelligent decision algorithm

2021 ◽  
Vol 39 (2) ◽  
pp. 367-374
Author(s):  
Ming Li ◽  
Qinghua Ren ◽  
Jialong Wu
Keyword(s):  
Stackelberg Game ◽  
Channel Selection ◽  
Interference Channel ◽  
Channel Switching ◽  
Decision Algorithm ◽  
Interference Level ◽  
Power Domain ◽  
Intelligent Decision ◽  
Channel Statistics

To understand the complex communication environment of a UAV in battlefield, its unknown channel statistics information and poor intelligent jamming and anti-jamming capability, the multi-domain anti-jamming problem is studied, and a multi-domain joint anti-jamming intelligent decision algorithm is proposed. First, the channel selection method is adopted to deal with jamming in the frequency domain. A multi-arm slot machine's channel selection model is established, and the channel interference level is judged. Secondly, the moderate interference channel is suppressed in the power domain, and the model of its Stackelberg game is established. The game equalization is solved to obtain the best transmission power and reduce the overhead caused by channel switching. The simulation results show that the long-term rewards of the intelligent decision algorithm are significantly higher than those of the traditional multi-arm slot machine's algorithm and the random selection algorithm and that the average throughput of the communication system of the UAV is improved, thus proving the superiority of the intelligent decision algorithm.

A PSO-based intelligent decision algorithm for VLSI floorplanning

2009 ◽  
Vol 14 (12) ◽  
pp. 1329-1337 ◽  
Author(s):  
Guolong Chen ◽  
Wenzhong Guo ◽  
Yuzhong Chen
Keyword(s):  
Decision Algorithm ◽  
Intelligent Decision ◽  
Vlsi Floorplanning

Decision making for highway complex scenario by improved safety field with learning process

2021 ◽  
pp. 095440702110532
Author(s):  
Can Xu ◽  
Wanzhong Zhao ◽  
Jingqiang Liu ◽  
Feng Chen
Keyword(s):  
Decision Making ◽  
Learning Process ◽  
Moving Objects ◽  
Learning Algorithm ◽  
Autonomous Vehicle ◽  
Kinematic Model ◽  
Action Sequence ◽  
Decision Algorithm ◽  
The Road

To improve the agility and efficiency of the highway decision-making system and overcome the local optimal dilemma of the existing safety field, this paper builds an improved safety field to reflect the advantage of the reachable states and the learning process is further employed to make the decision long-term optimal. Firstly, the improved safety field is prepared by the kinematic model-based prediction of surrounding vehicles and the boundary is determined elaborately to ensure real-time performance. Then, the field is constructed by three individual fields. One is the kinematic field, which is built based the safe-distance model to measure the colliding risk of both moving or no-moving objects accurately. Another is the road field that reflects the lane-marker constraint. The last is the efficiency field, which is introduced creatively to improve efficiency. Furthermore, the learning algorithm is adopted to learn the long-term optimal state-action sequence in the safety field. Finally, the simulations are conducted in Prescan platform to validate the feasibility of the improved safety field in complex scenarios. The results show that the proposed decision algorithm can always drive autonomous vehicle to the state with a long-term optimal payoff and can improve the overall performance compared to the existing pure safety field and the interaction-aware method.

scholarly journals Incentive Mechanism for Sustainable Improvement in a Supply Chain

2019 ◽  
Vol 11 (13) ◽  
pp. 3508 ◽  
Author(s):  
EuiBeom Jeong ◽  
GeunWan Park ◽  
Seung Ho Yoo
Keyword(s):  
Supply Chain ◽  
Environmental Quality ◽  
Stackelberg Game ◽  
Wholesale Price ◽  
Optimal Solution ◽  
Incentive Contract ◽  
Sustainable Improvement ◽  
Profit Performance

In this study, we consider the issue of sustainable development in the supply chain consisting of an original equipment manufacturer (OEM) and a contract manufacturer (CM). We investigate how to facilitate the CM’s investment in the environmental quality of a product so as to properly respond to climate change. We introduce a quantity incentive contract, and obtain the optimal solution based on a Stackelberg game. The OEM, as the focal company, determines the level of the incentive, and the CM, responsible for product design and production, determines its level of environmental quality given the OEM’s incentive offer. To investigate the effectiveness of the quantity incentive contract and provide important implications, we analytically compare the quantity incentive contract with the basic wholesale price contract without any incentives and conduct numerical experiments. Our results reveal that the quantity incentive contract facilitates the CM’s investment in environmental quality, and enhances the environmental, market, and profit performance of not only the CM but also the OEM which pays the incentive. We also show that the quantity incentive contract is suitable to develop a long-term relationship between the OEM and the CM.

scholarly journals Reliable, Efficient, and Power Optimized Control-Channel Selection Scheme for Cognitive Radio Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Saleem Aslam ◽  
Adnan Shahid ◽  
Kyung Geun Lee
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Bit Error Rate ◽  
Error Rate ◽  
Channel Selection ◽  
Radio Networks ◽  
Control Channel ◽  
Centralized Control ◽  
Channel Switching ◽  
Selection Scheme

This paper presents a centralized control-channel selection scheme for cognitive radio networks (CRNs) by exploiting the variation in the spectrum across capacity, occupancy, and error rate. We address the fundamental challenges in the design of the control-channel for CRNs: (1) random licensed users (LUs) activity and (2) the economical and vulnerability concerns for a dedicated control-channel. We develop a knapsack problem (KP) based reliable, efficient, and power optimized (REPO) control-channel selection scheme with an optimal data rate, bit error rate (BER), and idle time. Moreover, we introduce the concept of the backup channels in the context of control-channel selection, which assists the CRs to quickly move on to the next stable channel in order to cater for the sudden appearance of LUs. Based on the KP and its dynamic programming solution, simulation results show that the proposed scheme is highly adaptable and resilient to random LU activity. The REPO scheme reduces collisions with the LUs, minimizes the alternate channel selection time, and reduces the bit error rate (BER). Moreover, it reduces the power consumed during channel switching and provides a performance, that is, competitive with those schemes that are using a static control-channel for the management of control traffic in CRNs.

scholarly journals Body Schema in Autonomous Agents

2021 ◽  
Vol 08 (01) ◽  
pp. 113-145
Author(s):  
Zachariah A. Neemeh ◽  
Christian Kronsted ◽  
Sean Kugele ◽  
Stan Franklin
Keyword(s):  
Autonomous Agents ◽  
Cognitive Architecture ◽  
Body Schema ◽  
Dorsal Stream ◽  
The Body ◽  
Intelligent Decision ◽  
Computational Implementation ◽  
Global Workspace ◽  
Global Workspace Theory

A body schema is an agent’s model of its own body that enables it to act on affordances in the environment. This paper presents a body schema system for the Learning Intelligent Decision Agent (LIDA) cognitive architecture. LIDA is a conceptual and computational implementation of Global Workspace Theory, also integrating other theories from neuroscience and psychology. This paper contends that the ‘body schema’ should be split into three separate functions based on the functional role of consciousness in Global Workspace Theory. There is (1) an online model of the agent’s effectors and effector variables (Current Body Schema), (2) a long-term, recognitional storage of embodied capacities for action and affordances (Habitual Body Schema), and (3) “dorsal” stream information feeding directly from early perception to sensorimotor processes (Online Body Schema). This paper then discusses how the LIDA model of the body schema explains several experiments in psychology and ethology.

scholarly journals The Farmers’ Channel Selection and Sustainable Analysis under Carbon Tax Policy

2019 ◽  
Vol 11 (10) ◽  
pp. 2765 ◽  
Author(s):  
Cong Zheng ◽  
Quangui Pang ◽  
Tianpei Li ◽  
Guizheng Wang ◽  
Yiji Cai ◽  
...  
Keyword(s):  
Supply Chain ◽  
Carbon Emissions ◽  
Stackelberg Game ◽  
Carbon Tax ◽  
Product Yield ◽  
Channel Selection ◽  
Low Carbon ◽  
Deterioration Rate ◽  
Dual Channels ◽  
The Government

This paper examines a farmer’s channel selection in a supply chain led by a retailer, considering carbon emissions and products’ deterioration. Three channels—online channels, retail channels, and dual channels—are proposed. The inventory model of perishable products and the two-stage Stackelberg game model are used to illustrate the operational process. To compare performances of the three channel structures, we further determine the critical points consisting of the profits and the carbon emissions among these channels. The results provide useful insights for supply chain members and the government. Farmers can choose a channel to optimize profit with respect to deterioration rate and product yield, but it might conflict with the aim of least carbon emissions. When the deterioration rate is high, the online channel is not a suitable choice. For the government, the carbon tax contributes to the reduction of carbon emissions, but it also leads to the loss of the farmer’s profit. Additionally, numerical results further illustrate that, from the perspective of the government, transporting and inventory processes are two major sources of emissions, and it is essential to implement carbon tax and exploit low-carbon transportation.

scholarly journals Reinforcement Learning-Based Routing Protocol to Minimize Channel Switching and Interference for Cognitive Radio Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-24
Author(s):  
Tauqeer Safdar Malik ◽  
Mohd Hilmi Hasan
Keyword(s):  
Reinforcement Learning ◽  
Cognitive Radio ◽  
Routing Protocol ◽  
Channel Selection ◽  
Network Simulator ◽  
Mac Layer ◽  
Channel Switching ◽  
Network Layer ◽  
Switching Event ◽  
Channel Information

In the existing network-layered architectural stack of Cognitive Radio Ad Hoc Network (CRAHN), channel selection is performed at the Medium Access Control (MAC) layer. However, routing is done on the network layer. Due to this limitation, the Secondary/Unlicensed Users (SUs) need to access the channel information from the MAC layer whenever the channel switching event occurred during the data transmission. This issue delayed the channel selection process during the immediate routing decision for the channel switching event to continue the transmission. In this paper, a protocol is proposed to implement the channel selection decisions at the network layer during the routing process. The decision is based on past and expected future routing decisions of Primary Users (PUs). A learning agent operating in the cross-layer mode of the network-layered architectural stack is implemented in the spectrum mobility manager to pass the channel information to the network layer. This information is originated at the MAC layer. The channel selection is performed on the basis of reinforcement learning algorithms such as No-External Regret Learning, Q-Learning, and Learning Automata. This leads to minimizing the channel switching events and user interferences in the Reinforcement Learning- (RL-) based routing protocol. Simulations are conducted using Cognitive Radio Cognitive Network simulator based on Network Simulator (NS-2). The simulation results showed that the proposed routing protocol performed better than all the other comparative routing protocols in terms of number of channel switching events, average data rate, packet collision, packet loss, and end-to-end delay. The proposed routing protocol implies the improved Quality of Service (QoS) of the delay sensitive and real-time networks such as Cellular and Tele Vision (TV) networks.

Publisher’s distribution channel selection of print books in a book supply chain with print and e-books

2021 ◽  
Vol 12 (3) ◽  
pp. 358-385
Author(s):  
Yuwen Zeng ◽  
Wenhua Hou
Keyword(s):  
Stackelberg Game ◽  
Distribution Channel ◽  
Channel Selection ◽  
Third Party ◽  
Digital Products ◽  
Content Type ◽  
Print Books ◽  
Structured Analysis ◽  
Channel Mode ◽  
Selection Of

Purpose This paper aims to exam the publisher’s online distribution strategies of print books between a reselling and a marketplace channel with the coexistence of e-book. This study extends the study of channel selection to the content products industry. Design/methodology/approach By constructing a publisher-leader Stackelberg game model, the authors investigate the publisher’s distribution strategies. The retailer holds a digital channel for e-book and reselling and marketplace channels for print books. The authors examine three-channel modes for the print book distribution: a pure reselling channel, a marketplace channel and a hybrid channel. Findings The results reveal that a hybrid channel always dominates a pure marketplace channel from the publisher’s perspective. Then, only when the print book’s margin cost and the marketplace’s slotting fee are not very high, the publisher prefers the hybrid to a pure reselling channel. The authors also found a Pareto zone where the hybrid channel mode improves publisher’s and retailer’s profits. Furthermore, the publisher is less likely to choose the hybrid channel as the acceptance of e-book increases. The authors also examine the situation where a publisher-authorized third-party distributor runs the marketplace channel and found the results still hold. Originality/value This paper fills a theoretical and practical gap for a structured analysis of the content providers’ online distribution channel selection of the physical products and digital products. Different from previous related studies, this study focuses on analyzing physical products’ channel strategies and finds physical products’ cost plays a crucial role in the content provider’s channel decision.

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