^a, ^b Abstract
In this paper, an optimal motion/force hybrid control strategy based on adaptive reinforcement learning (ARL) is proposed for cooperating manipulator systems. The optimal trajectory control and constraint force factor control, by using the Moore–Penrose pseudoinverse, are addressed to design the controller corresponding to the manipulator dynamic model. In addition, a frame of a different auxiliary term and an appropriate state-variable vector are presented to address the non-autonomous closed system with a time-varying desired trajectory. The simultaneous actor/critic algorithm is implemented by optimizing the squared Bellman difference to be computed from the error of control policy and optimal control input. Moreover, the constraint force factor controller is discussed by a nonlinear technique after achieving the result of the constraint force factor. The tracking and convergence of ARL-based optimal motion/force hybrid control strategy are validated in closed-loop systems by a proposed Lyapunov function candidate. Finally, simulation results are implemented on a constrained system using three manipulators to verify the physical implementation of the presented optimal motion/force hybrid-tracking control strategy.

Hung Quach ^a, Jin-Jae Kim ^a, Duc-Toan Nguyen ^b, Young-Suk Kim ^a

Abstract

Truong Thu Huong a, Ta Phuong Bac b, Dao Minh Long a, Tran Duc Luong a, Nguyen Minh Dan a, Le Anh Quang a, Le Thanh Cong a, Bui Doan Thang a, Kim Phuc Tran c

Abstract

In recent years, the rapid development and wide application of advanced technologies have profoundly impacted industrial manufacturing, leading to smart manufacturing (SM). However, the Industrial IoT (IIoT)-based manufacturing systems are now one of the top industries targeted by a variety of attacks. In this research, we propose detecting Cyberattacks in Industrial Control Systems using Anomaly Detection. An anomaly detection architecture for the IIoT-based SM is proposed to deploy one of the top most concerned networking technique - a Federated Learning architecture - that can detect anomalies for  typically running inside an industrial system. The architecture achieves higher detection performance compared to the current detection solution for time series data. It also shows the feasibility and efficiency to be deployed on top of edge computing hardware of an IIoT-based SM that can save 35% of bandwidth consumed in the transmission link between the edge and the cloud. At the expense, the architecture needs to trade off with the computing resource consumed at edge devices for implementing the detection task. However, findings in maximal CPU usage of 85% and average Memory usage of 37% make this architecture totally realizable in an IIoT-based SM.

1. Highly Selective H2S Gas Sensor Based on WO3-Coated Sno2 Nanowires

  Tran Thi Ngoc Hoa ^a,b, Dang Thi Thanh Le ^a, Nguyen Van Toan ^a,b,c, Nguyen Van Duy ^a, Chu Manh Hung ^a, Nguyen Van Hieu ^c, Nguyen Duc Hoa ^a

a: International Training Institute for Materials Science, Hanoi University of Science and Technology, 1 Dai Co Viet, Hai Ba Trung, Hanoi, Vietnam

b: Hanoi Medical University, 1 Ton That Tung, Dong Da, Hanoi, Vietnam

c: Faculty of Electrical and Electronic Engineering, Phenikaa University, Yen Nghia, Hanoi, Vietnam

Abstract

The enhancement of the H₂S gas-sensing performance of SnO₂ nanowires is vital for practical application. In this study, H₂S gas sensors based on WO₃-coated SnO₂ nanowires were fabricated through a two-step process, namely, the chemical vapor deposition of SnO₂ nanowires and then coating with WO₃ by sputtering method. The morphology and crystal structures of the SnO₂ nanowires coated with WO3 were investigated by field-emission scanning electron microscopy, transmission electron microscopy, and energy-dispersive X-ray spectroscopy. The H₂S gas-sensing properties of the fabricated sensors were tested at temperatures of 150–250°C. The SnO₂ nanowires coated with 5 nm WO₃ showed the best response to low-concentration H₂S gas (0.1–1 ppm). At the optimal working temperature of 200°C, the sensor had a sensitivity of 177 toward 1 ppm H₂S with good selectivity over the contamination of NO₂, NH₃, H₂, and CO gases. We also discussed the gas-sensing mechanism of the fabricated sensor based on the n–n heterojunction between n-type SnO₂ and n-type WO₃, which formed a thick depletion layer and thus enhanced the sensitivity to H₂S. ()

2. Novel Numerical Approach for Free Vibration of Nanocomposite Joined Conical–Cylindrical–Conical Shells

    Dinh Gia Ninh ^a,b, Vu Tri Minh ^a,b, Nguyen Van Tuan ^c, Nguyen Chi Hung ^a,b, Dinh Van Phong ^a

a: School of Mechanical Engineering, Hanoi University of Science and Technology, 1 Dai Co Viet, Hai Ba Trung, Hanoi, Vietnam

b: Group of Materials and Structures, Hanoi University of Science and Technology, 1 Dai Co Viet, Hai Ba Trung, Hanoi, Vietnam

c: Department of Mechanical Engineering and Mechatronics, Phenikaa University, Yen Nghia, Hanoi, Vietnam

Abstract

The free vibration of conical–cylindrical shells made of carbon nanotube (CNT) reinforced to polymer is investigated by a semi-analytical method based on the theory of Donnell type with a moderately large deformation. The uniform and functionally graded CNTs are used to reinforce through the thickness of the shells. An exact power series solution using the regression formulas (TRF) is chosen to solve the equations of motion, and then boundary and continuity conditions is utilized to obtain the algebraic equations; therefore the fundamental frequencies are found. The accuracy of the method depends on the number of terms of the solution and the error of the computer. The codes of Wolfram Mathematica and MATLAB software are built to give the optimization of the results of the problem. The obtained results are compared with the previous results for the models including cylindrical shells, conical shells, and conical–cylindrical shells. Furthermore, the obtained results have good agreement with the experimental and Finite Element Analysis (FEA) results. The effects of geometrical parameters and material characteristics are carefully taken into account in the present study. The obtained results can be used as benchmark solutions for serving in further research and have the valuable applications in submarine shell, UAV, shells in the aerospace and civil engineering. ()

3. Evolutionary Algorithms to Optimize Task Scheduling Problem for the IoT Based Bag-of-Tasks Application in Cloud-Fog Computing Environment

    Binh Minh Nguyen ^a, Huynh Thi Thanh Binh ^a, Tran The Anh ^b, Do Bao Son ^c

a: School of Information and Communication Technology, Hanoi University of Science and Technology, 1 Dai Co Viet, Hai Ba Trung, Hanoi, Vietnam

b: School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang avenue, Singapore 639798, Singapore

c: Faculty of Information Technology, University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Hanoi, Vietnam

Abstract

In recent years, constant developments in Internet of Things (IoT) generate large amounts of data, which put pressure on Cloud computing’s infrastructure. The proposed Fog computing architecture is considered the next generation of Cloud Computing for meeting the requirements posed by the device network of IoT. One of the obstacles of Fog Computing is distribution of computing resources to minimize completion time and operating cost. The following study introduces a new approach to optimize task scheduling problem for Bag-of-Tasks applications in Cloud–Fog environment in terms of execution time and operating costs. The proposed algorithm named TCaS was tested on 11 datasets varying in size. The experimental results show an improvement of 15.11% compared to the Bee Life Algorithm (BLA) and 11.04% compared to Modified Particle Swarm Optimization (MPSO), while achieving balance between completing time and operating cost. ()

1. One-Step Fabrication of SnO2 Porous Nanofiber Gas Sensors for Sub-ppm H2S Detection

    Phan Hong Phuoc a, Chu Manh Hung a, Nguyen Van Toan a, Nguyen Van Duy a, Nguyen Duc Hoa a, Nguyen Van Hieu b,c   

a: International Training Institute for Materials Science, Hanoi University of Science and Technology, 1 Dai Co Viet, Hai Ba Trung, Hanoi, Vietnam

b: Faculty of Electrical and Electronic Engineering, Phenikaa Institute for Advanced Study (PIAS), Phenikaa University, Yen Nghia, Hanoi, Vietnam

c: Phenikaa Research and Technology Institute (PRATI), A&A Green Phoenix Group, 167 Hoang Ngan, Hanoi, Vietnam

Abstract

SnO2 porous nanofibers (NFs) were deposited on-chip by using a facile electrospinning method followed by heat treatment at 600oC and used to detect H2S concentrations at sub-parts per million level. Morphological, compositional, crystal, and atomic structural properties of the as-spun and calcined SnO2 NFs were investigated by field emission electron microscopy, energy dispersive spectroscopy, X-ray diffraction, and high-resolution transmission electron microscopy, respectively. SnO2 porous NFs with an average diameter of 150 nm and consisting of many nanograins were successfully fabricated by on-chip electrospinning. The NFs were crystallized as the tetragonal structure of SnO2 with an average crystallite size and dislocation density of approximately 13.5 nm and 5.615 × 1015 lines/m2, respectively. The sensing characteristics of the SnO2 NF sensors were tested with 0.1–1 ppm H2S from 150oC to 450oC. The sensor achieved the optimal performance at 350oC and exhibited gas response of 15.2 with fast response/recovery times of 15 s/230 s. The H2S gas sensing mechanisms of the SnO2 porous NF sensors were due to the modulation of the resistance along the surface depletion layer and the grain boundaries. The fabricated sensor also indicated a good selectivity to H2S, short-term stability, and the low detection limit of 1.6 ppb. The influence of humidity on the sensor’s performance in a low temperature range is also discussed. ()

2. An Efficient Genetic Algorithm for Maximizing Area Coverage in Wireless Sensor Networks

    Nguyen Thi Hanh a,b, Huynh Thi Thanh Binh a, Nguyen Xuan Hoai c, Marimuthu Swami Palaniswami d 

a: Hanoi University of Science and Technology, 1 Dai Co Viet, Hai Ba Trung, Hanoi, Vietnam

b: Phuong Dong University, 171 Trung Kinh, Cau Giay, Hanoi, Vietnam

c: AI Academy, Vietnam, 489 Hoang Quoc Viet, Cau Giay, Hanoi, Vietnam 

d: University of Melbourne, Grattan Street, Parkville, Victoria, 3010, Australia

Abstract

Wireless sensor networks collect and transfer environmental data from a predefined region to a base station to be processed and analyzed. A major problem when designing these networks is deploying sensors such that their area coverage is maximized. Given a number of sensors with heterogeneous sensing ranges, the problem of coverage maximization is known to be NP-hard. As such, prevailing methods often rely on metaheuristic techniques while employing approximated fitness functions, resulting in modest solution quality and stability. This paper proposes a novel and efficient metaheuristic in the form of a genetic algorithm, which overcomes several weaknesses of existing metaheuristics, along with an exact method for calculating the fitness function for this problem. The proposed genetic algorithm includes a heuristic population initialization procedure, the proposed exact integral area calculation for the fitness function, and a combination of Laplace Crossover and Arithmetic Crossover Method operators. Experiments have been conducted to compare the proposed algorithm with five state-of-the-art methods on a wide range of problem instances. The results show that our algorithm delivers the best performance in terms of solution quality and stability on a majority of the tested instances. ()

3. Intrinsic and Tunable Ferromagnetism in Bi0.5Na0.5TiO3 through CaFeO3-δ Modification

    N. T. Hung a, N. H. Lam a, A. D. Nguyen b,c, L. H. Bac a, N. N. Trung a, D. D. Dung a, Y. S. Kim c, N. Tsogbadrakh d, T. Ochirkhuyag e, D. Odkhuu e

a: School of Engineering Physics, Hanoi University of Science and Technology, 1 Dai Co Viet, Hai Ba Trung, Hanoi, Vietnam

b: Department of Physics, Faculty of Basic-Fundamental Sciences, Viet Nam Maritime University, 484 Lach Tray, Le Chan, Haiphong, Vietnam

c: Department of Physics, University of Ulsan, Ulsan, 680-749, Republic of Korea

d: Department of Physics, National University of Mongolia, Ulaanbaatar, 14201, Mongolia

e: Department of Physics, Incheon National University, Incheon, 22012, Republic of Korea

Abstract

New (1-x) Bi0.5Na0.5TiO3 +xCaFeO3-δ solid solution compounds were fabricated using a sol–gel method. the CaFeO3-δ materials were mixed into host Bi0.5Na0.5TiO3 materials to form a solid solution that exhibited similar crystal symmetry to those of Bi0.5Na0.5TiO3 phases. The random distribution of Ca and Fe cations in the Bi0.5Na0.5TiO3 crystals resulted in a distorted structure. The optical band gaps decreased from 3.11 eV for the pure Bi0.5Na0.5TiO3 samples to 2.34 eV for the 9 mol% CaFeO3-δ-modified Bi0.5Na0.5TiO3 samples. Moreover, the Bi0.5Na0.5TiO3 samples exhibited weak photoluminescence because of the intrinsic defects and suppressed photoluminescence with increasing CaFeO3-δ concentration. Experimental and theoretical studies via density functional theory calculations showed that pure Bi0.5Na0.5TiO3 exhibited intrinsic ferromagnetism, which is associated with the possible presence of Bi, Na, and Ti vacancies and Ti3+-defect states. Further studies showed that such an induced magnetism by intrinsic defects can also be enhanced effectively with CaFeO3-δ addition. This study provides a basis for understanding the role of secondary phase as a solid solution in Bi0.5Na0.5TiO3 to facilitate the development of lead-free ferroelectric materials. ()