An optimal schedule model of multi-energy hubs network integrating solar energy

Recently, multi-energy systems based on energy hub are introduced because of significant benefits in reducing energy and emission cost. This paper proposed an optimal schedule model of multi-energy hubs networks consisting of energy hubs, renewable sources, and energy storage which are connected by electrical and natural gas distribution networks. In the proposed mixed-integer nonlinear programming model, the objective is to minimize the operation, energy, and emission costs of energy hubs with both renewable sources and storage and energy distribution networks. The proposed schedule framework allows simultaneously selections of optimal operation structure of EHs together with the optimal operation parameters of energy distribution networks and therefore this model can maximize the profit of the entire large-scale multi-energy hubs network. Besides, the operation parameters and energy loss of both electrical and natural gas distribution networks are considered in conjunction with optimal operation of energy hubs and thus guarantee the operation and optimization of the network in all operational scenarios. The IEEE 5-bus test system is utilized to demonstrate the applicability of the proposed model. The simulation results show the feasibility of the proposed model, and demonstrate that the energy hubs, renewable sources, and energy storage in the proposed structure significantly enhance the efficiency of the multi-energy hubs network by reducing not only energy and operation costs but also emission

Role of Scientific Research for Lecturers of Current Universities

Scientific research for university lecturers plays an important role in training creative thinking ability, research capacity and scientific working style  for researchers. This contributes to clarifying some scientific issues and solving practical problems that arise in order to improve the quality of teaching. This article focuses on analyzing some issues about the role of scientific research for university lecturers today

Power Optimization With BLER Constraint for Wireless Fronthauls in C-RAN

Cloud radio access network (C-RAN) is a novel architecture for future mobile networks to sustain the exponential traffic growth thanks to the exploitation of centralized processing. In C-RAN, one data processing center or baseband unit (BBU) communicates with users via distributed remote radio heads (RRHs), which are connected to the BBU via high capacity, low latency fronthaul links. In this letter, we study C-RAN with wireless fronthauls due to their flexibility in deployment and management. First, a tight upper bound of the system block error rate (BLER) is derived in closed-form expression via union bound analysis. Based on the derived bound, adaptive transmission schemes are proposed. Particularly, two practical power optimizations based on the BLER and pair-wise error probability (PEP) are proposed to minimize the consumed energy at the RRHs while satisfying the predefined quality of service (QoS) constraint. The premise of the proposed schemes originates from practical scenarios where most applications tolerate a certain QoS, e.g., a nonzero BLER. The effectiveness of the proposed schemes is demonstrated via intensive simulations

Improvement of Tuning Fork Gyroscope Drive-mode Oscillation Matched using a Differential Driving Suspension Frame

This paper presents a novel design of a vibration tuning fork gyroscope (TFG) based on a differential driving suspension coupling spring between two gyroscopes. The proposed TFG is equivalent to a transistor differential amplifier circuit. The mechanical vibrations of driving frames are, therefore, well matched. The matching level depends on stiffness of spring. When three various TFG structures respond to differential stiffness of spring, their the driving frame mechanical vibration is well matched in case the input excitation driving differential phase is less than 3.5°, 2.5°, and 4°, respectively. The fabricated tuning fork gyroscope linearly operates in the range from -200 to +200 degree/s with the resolution of about 0.45 mV/degree/s

ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing

English and Chinese, known as resource-rich languages, have witnessed the strong development of transformer-based language models for natural language processing tasks. Although Vietnam has approximately 100M people speaking Vietnamese, several pre-trained models, e.g., PhoBERT, ViBERT, and vELECTRA, performed well on general Vietnamese NLP tasks, including POS tagging and named entity recognition. These pre-trained language models are still limited to Vietnamese social media tasks. In this paper, we present the first monolingual pre-trained language model for Vietnamese social media texts, ViSoBERT, which is pre-trained on a large-scale corpus of high-quality and diverse Vietnamese social media texts using XLM-R architecture. Moreover, we explored our pre-trained model on five important natural language downstream tasks on Vietnamese social media texts: emotion recognition, hate speech detection, sentiment analysis, spam reviews detection, and hate speech spans detection. Our experiments demonstrate that ViSoBERT, with far fewer parameters, surpasses the previous state-of-the-art models on multiple Vietnamese social media tasks. Our ViSoBERT model is available only for research purposes.Comment: Accepted at EMNLP'2023 Main Conferenc

EVALUATION OF BIODEGRADATION RATE CONSTANT (K₁) AND BOD POLLUTION IN THE LAKE SYSTEM OF HANOI

Joint Research on Environmental Science and Technology for the Eart

Improvement of Bit Rate Using M-ary Chaotic Pulse Position Modulation

Recent studies have pointed out that Chaotic Pulse Position Modulation (CPPM) is a very promising method for improving privacy and security in chaos-based digital communication. Especially, CPPM provides better performance than other chaotic modulation methods in noise- and distortion-affected environments. In this paper we present our development of a robust method named M-ary CPPM which is based on the combination of the conventional CPPM and multi-symbol modulation in order to improve the transmission bit rate. The M-ary CPPM signal has a pulse train format in which each pulse is a symbol and the chaotically-varied inter-pulse time interval conveys the binary information of k bits (M = 2k). The analysis and development of modulation and demodulation schemes are presented in detail. Theoretical evaluation of Bit-Error-Rate (BER) performance in the presence of additive white Gaussian noise (AWGN) and the use of AWGN filtering is also provided. The chaotic behavior of the M-ary CPPM is investigated with the variation of modulation parameters. In order to verify the performance of the proposed schemes, numerical simulations were carried out in Simulink and comparison between simulation and theoretical results is reported

UAV Relay-Assisted Emergency Communications in IoT Networks: Resource Allocation and Trajectory Optimization

In this paper, a UAV is deployed as a flying base station to collect data from time-constrained IoT devices and then transfer the data to a ground gateway (GW). In general, the latency constraint at IoT users and the limited storage capacity of UAV highly hinder practical applications of UAV-assisted IoT networks. In this paper, full-duplex (FD) technique is adopted at the UAV to overcome these challenges. In addition, half-duplex (HD) scheme for UAV-based relaying is also considered to provide a comparative study between two modes. In this context, we aim at maximizing the number of served IoT devices by jointly optimizing bandwidth and power allocation, as well as the UAV trajectory, while satisfying the requested timeout (RT) requirement of each device and the UAV's limited storage capacity. The formulated optimization problem is troublesome to solve due to its non-convexity and combinatorial nature. Toward appealing applications, we first relax binary variables into continuous values and transform the original problem into a more computationally tractable form. By leveraging inner approximation framework, we derive newly approximated functions for non-convex parts and then develop a simple yet efficient iterative algorithm for its solutions. Next, we attempt to maximize the total throughput subject to the number of served IoT devices. Finally, numerical results show that the proposed algorithms significantly outperform benchmark approaches in terms of the number of served IoT devices and the amount of collected data.Comment: 30 pages, 11 figure

STUDY ON REMOVING COLOR IN THE EXTRACT SOLUTION FROM VEGETABLES FOR ANALYZING ORGANOCHLORIDE PESTICIDES RESIDUE IN HANOI MARKETS

Joint Research on Environmental Science and Technology for the Eart