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Biologically inspired modular neural control for a leg-wheel hybrid robot

  • Manoonpong, Poramate;Worgotter, Florentin;Laksanacharoen, Pudit
    • Advances in robotics research
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    • v.1 no.1
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    • pp.101-126
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    • 2014
  • In this article we present modular neural control for a leg-wheel hybrid robot consisting of three legs with omnidirectional wheels. This neural control has four main modules having their functional origin in biological neural systems. A minimal recurrent control (MRC) module is for sensory signal processing and state memorization. Its outputs drive two front wheels while the rear wheel is controlled through a velocity regulating network (VRN) module. In parallel, a neural oscillator network module serves as a central pattern generator (CPG) controls leg movements for sidestepping. Stepping directions are achieved by a phase switching network (PSN) module. The combination of these modules generates various locomotion patterns and a reactive obstacle avoidance behavior. The behavior is driven by sensor inputs, to which additional neural preprocessing networks are applied. The complete neural circuitry is developed and tested using a physics simulation environment. This study verifies that the neural modules can serve a general purpose regardless of the robot's specific embodiment. We also believe that our neural modules can be important components for locomotion generation in other complex robotic systems or they can serve as useful modules for other module-based neural control applications.

An Energy Efficient Intelligent Method for Sensor Node Selection to Improve the Data Reliability in Internet of Things Networks

  • Remesh Babu, KR;Preetha, KG;Saritha, S;Rinil, KR
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.9
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    • pp.3151-3168
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    • 2021
  • Internet of Things (IoT) connects several objects with embedded sensors and they are capable of exchanging information between devices to create a smart environment. IoT smart devices have limited resources, such as batteries, computing power, and bandwidth, but comprehensive sensing causes severe energy restrictions, lowering data quality. The main objective of the proposal is to build a hybrid protocol which provides high data quality and reduced energy consumption in IoT sensor network. The hybrid protocol gives a flexible and complete solution for sensor selection problem. It selects a subset of active sensor nodes in the network which will increase the data quality and optimize the energy consumption. Since the unused sensor nodes switch off during the sensing phase, the energy consumption is greatly reduced. The hybrid protocol uses Dijkstra's algorithm for determining the shortest path for sensing data and Ant colony inspired variable path selection algorithm for selecting active nodes in the network. The missing data due to inactive sensor nodes is reconstructed using enhanced belief propagation algorithm. The proposed hybrid method is evaluated using real sensor data and the demonstrated results show significant improvement in energy consumption, data utility and data reconstruction rate compared to other existing methods.

A Genome-Scale Co-Functional Network of Xanthomonas Genes Can Accurately Reconstruct Regulatory Circuits Controlled by Two-Component Signaling Systems

  • Kim, Hanhae;Joe, Anna;Lee, Muyoung;Yang, Sunmo;Ma, Xiaozhi;Ronald, Pamela C.;Lee, Insuk
    • Molecules and Cells
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    • v.42 no.2
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    • pp.166-174
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    • 2019
  • Bacterial species in the genus Xanthomonas infect virtually all crop plants. Although many genes involved in Xanthomonas virulence have been identified through molecular and cellular studies, the elucidation of virulence-associated regulatory circuits is still far from complete. Functional gene networks have proven useful in generating hypotheses for genetic factors of biological processes in various species. Here, we present a genome-scale co-functional network of Xanthomonas oryze pv. oryzae (Xoo) genes, XooNet (www.inetbio.org/xoonet/), constructed by integrating heterogeneous types of genomics data derived from Xoo and other bacterial species. XooNet contains 106,000 functional links, which cover approximately 83% of the coding genome. XooNet is highly predictive for diverse biological processes in Xoo and can accurately reconstruct cellular pathways regulated by two-component signaling transduction systems (TCS). XooNet will be a useful in silico research platform for genetic dissection of virulence pathways in Xoo.

Anthropomorphic Animal Face Masking using Deep Convolutional Neural Network based Animal Face Classification

  • Khan, Rafiul Hasan;Lee, Youngsuk;Lee, Suk-Hwan;Kwon, Oh-Jun;Kwon, Ki-Ryong
    • Journal of Korea Multimedia Society
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    • v.22 no.5
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    • pp.558-572
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    • 2019
  • Anthropomorphism is the attribution of human traits, emotions, or intentions to non-human entities. Anthropomorphic animal face masking is the process by which human characteristics are plotted on the animal kind. In this research, we are proposing a compact system which finds the resemblance between a human face and animal face using Deep Convolutional Neural Network (DCNN) and later applies morphism between them. The whole process is done by firstly finding which animal most resembles the particular human face through a DCNN based animal face classification. And secondly, doing triangulation based morphing between the particular human face and the most resembled animal face. Compared to the conventional manual Control Point Selection system using an animator, we are proposing a Viola-Jones algorithm based Control Point selection process which detects facial features for the human face and takes the Control Points automatically. To initiate our approach, we built our own dataset containing ten thousand animal faces and a fourteen layer DCNN. The simulation results firstly demonstrate that the accuracy of our proposed DCNN architecture outperforms the related methods for the animal face classification. Secondly, the proposed morphing method manages to complete the morphing process with less deformation and without any human assistance.

Apply Blockchain to Overcome Wi-Fi Vulnerabilities

  • Kim, Seong-Kyu (Steve)
    • Journal of Multimedia Information System
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    • v.6 no.3
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    • pp.139-146
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    • 2019
  • This paper, wireless internet such as Wi-Fi has a vulnerability to security. Blockchain also means a 'Ledger' in which transaction information that occurs on a public or private network is encrypted and shared among the network participants. Blockchain maintains information integrity by making it impossible for a particular node to tamper with information arbitrarily, a feature that would result in changes in the overall blockchain hash value if any one transaction information that constitutes a block was changed. The complete sharing of information through a peer-to-peer network will also cripple hacking attempts from outside, targeting specialized nodes, and prepare for the "single point of failure" risk of the entire system being shut down. Due to the value of these Blockchain, various types of Blockchain are emerging, and related technology development efforts are also actively underway. Various business models such as public block chains such as Bitcoin, as well as private block chains that allow only certain authorized nodes to participate, or consortium block chains operated by a select few licensed groups, are being utilized. In terms of technological evolution, Blockchain also shows the potential to grow beyond cryptocurrency into an online platform that allows all kinds of transactions with the advent of 'Smart Contract'. By using Blockchain technology, the company makes suggestions to overcome the vulnerability of wireless Internet.

A Study on 5G Service Methods by using BOCR Model and ANP

  • Song, Inkuk
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.16 no.10
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    • pp.3299-3312
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    • 2022
  • Recently, South Korea preferentially allocated frequencies to build 5G networks as a core competitiveness of the 4th Industrial Revolution. Although the government recognize the importance of 5G construction and preoccupation, network operators have limited to some services, testing the possibility of practical use of 5G. They hesitated to actively build and to carry out the service of a complete 5G network. While 5G is being developed and standardized like this, no one is sure of this step exactly what 5G will be. Thus, following research questions are asked by various stakeholders of 5G market: What is an ideal service providing method for the practical use of 5th generation mobile network? And what are the critical elements to be considered when selecting the service providing method? Therefore, the study aims to investigate 5G service providing issues and elements to be considered and to provide most appropriate service providing method for the practical use of 5G. The results identify that 'Specialized Service' is most appropriate method at the aspects of benefit and opportunity as well as the aspect of risk. In addition, the outcomes imply that the experts replying to the survey not only expect the expansion of emerging market, but also concern the social risk and cost. Since the study dealt with economic, social and business issues in providing 5G service, it might contribute not only to practical research, but also to academic research regarding 5G service method.

Resistance Performance Simulation of Simple Ship Hull Using Graph Neural Network (그래프 신경망을 이용한 단순 선박 선형의 저항성능 시뮬레이션)

  • TaeWon, Park;Inseob, Kim;Hoon, Lee;Dong-Woo, Park
    • Journal of the Society of Naval Architects of Korea
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    • v.59 no.6
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    • pp.393-399
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    • 2022
  • During the ship hull design process, resistance performance estimation is generally calculated by simulation using computational fluid dynamics. Since such hull resistance performance simulation requires a lot of time and computation resources, the time taken for simulation is reduced by CPU clusters having more than tens of cores in order to complete the hull design within the required deadline of the ship owner. In this paper, we propose a method for estimating resistance performance of ship hull by simulation using a graph neural network. This method converts the 3D geometric information of the hull mesh and the physical quantity of the surface into a mathematical graph, and is implemented as a deep learning model that predicts the future simulation state from the input state. The method proposed in the resistance performance experiment of simple hull showed an average error of about 3.5 % throughout the simulation.

An ANN-based Intelligent Spectrum Sensing Algorithm for Space-based Satellite Networks

  • Xiujian Yang;Lina Wang
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.17 no.3
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    • pp.980-998
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    • 2023
  • In Low Earth Orbit (LEO) satellite networks, satellites operate fast and the inter-satellite link change period is short. In order to sense the spectrum state in LEO satellite networks in real-time, a space-based satellite network intelligent spectrum sensing algorithm based on artificial neural network (ANN) is proposed, while Geosynchronous Earth Orbit (GEO) satellites are introduced to make fast and effective judgments on the spectrum state of LEO satellites by using their stronger arithmetic power. Firstly, the visibility constraints between LEO satellites and GEO satellites are analyzed to derive the inter-satellite link building matrix and complete the inter-satellite link situational awareness. Secondly, an ANN-based energy detection (ANN-ED) algorithm is proposed based on the traditional energy detection algorithm and artificial neural network. The ANN module is used to determine the spectrum state and optimize the traditional energy detection algorithm. GEO satellites are used to fuse the information sensed by LEO satellites and then give the spectrum decision, thereby realizing the inter-satellite spectrum state sensing. Finally, the sensing quality is evaluated by the analysis of sensing delay and sensing energy consumption. The simulation results show that our proposed algorithm has lower complexity, the sensing delay and sensing energy consumption compared with the traditional energy detection method.

A Survey on the Mobile Crowdsensing System life cycle: Task Allocation, Data Collection, and Data Aggregation

  • Xia Zhuoyue;Azween Abdullah;S.H. Kok
    • International Journal of Computer Science & Network Security
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    • v.23 no.3
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    • pp.31-48
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    • 2023
  • The popularization of smart devices and subsequent optimization of their sensing capacity has resulted in a novel mobile crowdsensing (MCS) pattern, which employs smart devices as sensing nodes by recruiting users to develop a sensing network for multiple-task performance. This technique has garnered much scholarly interest in terms of sensing range, cost, and integration. The MCS is prevalent in various fields, including environmental monitoring, noise monitoring, and road monitoring. A complete MCS life cycle entails task allocation, data collection, and data aggregation. Regardless, specific drawbacks remain unresolved in this study despite extensive research on this life cycle. This article mainly summarizes single-task, multi-task allocation, and space-time multi-task allocation at the task allocation stage. Meanwhile, the quality, safety, and efficiency of data collection are discussed at the data collection stage. Edge computing, which provides a novel development idea to derive data from the MCS system, is also highlighted. Furthermore, data aggregation security and quality are summarized at the data aggregation stage. The novel development of multi-modal data aggregation is also outlined following the diversity of data obtained from MCS. Overall, this article summarizes the three aspects of the MCS life cycle, analyzes the issues underlying this study, and offers developmental directions for future scholars' reference.

Routing optimization algorithm for logistics virtual monitoring based on VNF dynamic deployment

  • Qiao, Qiujuan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.16 no.5
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    • pp.1708-1734
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    • 2022
  • In the development of logistics system, the breakthrough of important technologies such as technology platform for logistics information management and control is the key content of the study. Based on Javascript and JQuery, the logistics system realizes real-time monitoring, collection of historical status data, statistical analysis and display, intelligent recommendation and other functions. In order to strengthen the cooperation of warehouse storage, enhance the utilization rate of resources, and achieve the purpose of real-time and visual supervision of transportation equipment and cargo tracking, this paper studies the VNF dynamic deployment and SFC routing problem in the network load change scenario based on the logistics system. The BIP model is used to model the VNF dynamic deployment and routing problem. The optimization objective is to minimize the total cost overhead generated by each SFCR. Furthermore, the application of the SFC mapping algorithm in the routing topology solving problem is proposed. Based on the concept of relative cost and the idea of topology transformation, the SFC-map algorithm can efficiently complete the dynamic deployment of VNF and the routing calculation of SFC by using multi-layer graph. In the simulation platform based on the logistics system, the proposed algorithm is compared with VNF-DRA algorithm and Provision Traffic algorithm in the network receiving rate, throughput, path end-to-end delay, deployment number, running time and utilization rate. According to the test results, it is verified that the test results of the optimization algorithm in this paper are obviously improved compared with the comparison method, and it has higher practical application and promotion value.