Operation Algorithm Research Articles

컨테이너터미널의 이송장비 자동화(YT·AGV) 경제적 타당성 분석

Purpose - The purpose of this study is to understand the limitations of yard transfer equipment composition through the economic feasibility analysis of yard transfer equipment by examining the transport equipment operation algorithm and work process composition of terminals. Design/Methodology/Approach - Yard Transfer Equipment YT is widely used at domestic and international terminals. The Domestic G terminal is interested in the construction of yard transfer equipment automation. In the study, the actual transport equipment operation algorithm process and work process composition of Gwangyang G Terminal and L Terminal in China were investigated, and economic feasibility was investigated. Findings - As a result of economic feasibility analysis, it was shown that e terminal operated by YT transfer equipment lacked economic feasibility, but the terminal operated by AGV transfer equipment attained economic feasibility. In the case of terminals operated by AGV transfer equipment, the B/C ratio was 1.8, NPV was about 27 billion won, and IRR was 13. Therefore, the terminal operated by AGV transfer equipment has economic feasibility. Research Implications - It is expected that safety accidents, cost reduction, and efficient transfer equipment management will be possible through the economic performance of yard transfer equipment. In addition, an economic feasibility analysis was presented so that terminal managers can decide on efficient investments when deciding to introduce and build yard transfer equipment, and reflect this to actively cope with the automation of yard transfer equipment.

Jellyfish search algorithm for optimization operation of hybrid pumped storage-wind-thermal-solar photovoltaic systems

This study applies Jellyfish Search Algorithm and five other algorithms to minimize the electricity generation cost of two hybrid systems for one operating day. The first system comprises one pumped storage hydroelectric plant and two thermal power plants. The second system is expanded by integrating one wind and one solar photovoltaic power plant into the first system. For each system during one operating day, the pumped storage hydroelectric plant with only generation mode acts as a conventional hydroelectric plant in the first scenario. Still, it can run pumps to store water and produce electricity in the second scenario. As a result, JSA can reach smaller costs than all compared algorithms, from about 1 % to higher than 10 % for two scenarios in the two systems. The comparisons of generation cost indicate the second scenario with the pumped storage hydroelectric plant can reach a smaller cost than the first scenario with the conventional hydroelectric power plant by $53,359.7, corresponding to 7.4 % in the first system and $39,472.8, corresponding to 6.95 % in the second system. Therefore, the water storage function of the pumped storage hydroelectric plant is very effective in reducing the electricity generation costs for hybrid power systems.

Питание однофазного асинхронного электродвигателя от преобразователя частоты

The article discusses ways to connect a single-phase asynchronous motor to an AC electrical network. Their comparative analysis is given. The option of powering a single-phase asynchronous motor from a frequency converter driven by the network is considered. A schematic diagram of the converter, a description of the principle of operation, a control algorithm and timing diagrams of operation for various modes are given. The advantages of using a frequency converter driven by the network for powering single-phase asynchronous motors are substantiated

Development of optimal operation algorithm for rotation mechanism of refuelling machine television rod

The paper examines the issues of optimizing the operation of the rotation mechanism of the television rod of the refuelling machine during the refueling campaign during the scheduled preventive maintenance at the NPP. The task under consideration is to minimize the time spent on turning the television rod during a reloading campaign in order to save time and money on technological operations, and thereby reduce the downtime of the nuclear power plant unit. A description of the design and purpose of the work area, the refuelling machine and other objects involved in technological operations is provided. In all parts of the working area there is a certain number of «problem» cells in which interference and/or blocking of rotation of the television rod may occur. The exact location of all «problem» cells and the permissible angles of movement and observation for them are revealed. The list of operations is compiled as a table containing: operation numbers, markings of the corresponding cluster, as well as the initial and target positions in coordinates. This table comes complete with a cartogram with coordinates and cell labels. It is calculated how long it takes to turn from one permissible viewing angle to another. An example of time calculation for an operation with a reloading machine is given.

An Advanced Multi-Objective Ant Lion Algorithm for Reservoir Flood Control Optimal Operation

Multi-objective reservoir operation of reservoir flood control involves numerous factors and complex model solving, and exploring effective methods for solving the operation models has always been a hot topic in reservoir optimization operation research. The Multi-Objective Ant Lion Algorithm (MOALO) is an emerging heuristic intelligent optimization algorithm, but it has not yet been applied in reservoir optimization operation. Testing the effectiveness of this method on multi-objective reservoir scheduling and further improving the optimization performance of this method is of great significance for enhancing the overall benefits of reservoir operation. In this study, MOALO is applied to the optimal scheduling of reservoir flood control. To increase the search efficiency of MOLAO, the advanced MOALO method (AMOLAO) is proposed by reconstructing the search distribution in MOALO using a power function. Taking the Songshu Reservoir and Dongfeng Reservoir in the Fuzhou River Basin in Dalian City as an example, MOALO, AMOLAO, and other two traditional methods are applied for solving the multi-objective reservoir operation problem. Results show that the AMOALO method has high search efficiency, strong optimization ability, and good stability. AMOALO performs better than MOALO and the two traditional methods. The study provides an efficient method for solving the problems in multi-objective reservoir operation.

Toward a Renewable and Sustainable Energy Pattern in Non-Interconnected Rural Monasteries: A Case Study for the Xenofontos Monastery, Mount Athos

The scope of this article is to study and propose optimized electricity production plants powered by renewable energy sources, in the frame of energy transition in non-interconnected, rural monasteries. Energy transition, namely, the transition from fossil fuels to renewables and rational use of energy, constitutes a major component of sustainability. In particular, monasteries constitute a special and unique category of rural communities, given their size and the scale of the electricity demand. As a case study, this work focuses on the Xenofontos Monastery, in Mount Athos. Mount Athos, practically a mountainous peninsula at the North Aegean Sea (central-south Macedonia, Greece), is an independent and self-governed community of 20 different monasteries, with no electrical interconnection between them. The electrification of these monasteries started in the 1980s, with the installation of autonomous small diesel generators. Since 2010, an attempt has been initiated to replace these generators with power production and storage technologies based on renewable energy sources, aiming to approach a more energy-independent and sustainable pattern in the peninsula. The article examines two alternative systems, with small wind turbines and photovoltaic panels as the power production units and small pumped hydro storage or electrochemical batteries as storage technologies. New operation algorithms were developed and the sizing of the systems was accomplished through the computational simulation of the examined plants’ annual operations, aiming at full coverage of the power demand. The article proves that 100% power demand coverage from hydro power plants is possible with the support of pumped storage, achieving a Levelized Cost Of Electricity in the range of 0.22 EUR/kWh. This feature can be reduced at 0.11 EUR/kWh with the support of lithium-ion batteries, yet with annual power demand coverage at 90%.

Resource-saving microprocessor-based reed switch current protection

We suggest designing a cutoff and an overcurrent protection for 6–35 kV electrical installations with switchgear cubicles on the basis of reed switches and a microprocessor without current transformers. A reed switch is a small glass tube with plates inside, which contact under the action of the corresponding magnetic field. Two reed switches are mounted near each busbar inside a switchgear cubicle at a safe distance using specially designed structures. The magnetic induction which acts on them is determined by the Biot–Savart law with experimentally found coefficients. The effect of currents in neighboring electrical installations and in switchgear cubicle casings is taken into account. We have developed and describe here an algorithm of protection operation and a technique for selecting parameters and estimating protection sensitivity. Such protections are noise-resistant due to detection of short circuits by the third actuation of a read switch; they detect a failed component with the help of built-in diagnostics. The test diagnostics is performed by means of supplying current simultaneously to the control windings of two reed switches, and the functional diagnostics, by means of supplying current to them in turn. The efficiency of such protections is confirmed by the simulation results.

Аналитическая и компьютерная программная математические модели шума выходного сигнала волоконно-оптического гироскопа, анализ и верификация

The goal of this work is to identify the main source of noise in the output signal of a fiber-optic gyroscope (FOG) and ways to reduce it. To achieve the stated goal, two mathematical models of the noise component of the output signal of the FOG were developed. These models are based on fundamental expressions defining shot and thermal noise, as well as an expression for the excess noise of an optical radiation source. The general expression of the analytical model takes into account the four fundamental sources of noise in FOG. Their influence on the output signal noise is expressed through the relation between the measured optical radiation power and the angular velocity acting on the FOG. The computer model was developed in the “Octave” environment. Model performs cyclic execution of the FOG operation algorithm with a closed feedback loop for measuring angular velocity. In the computer model, noise from four independent sources is added to a given useful signal. An array of model output data is generated. The noise power spectral density is calculating. The paper analyzes the influence of FOG circuit parameters on the noise level. The noise source having the greatest significance in the output signal was identified. To confirm the correctness of the models, a comparison was made between the simulation results and the noise of commercially produced FOG. The model error is no more than 10%.

Development of modified P&O algorithm based simple controller for constant voltage and frequency operation of three-phase IIG for micro-hydro application

This paper introduces Modified Perturb and Observe (MP&O) based MPPT algorithm for constant frequency and voltage operation of a three-phase Isolated Induction Generator (IIG) coupled to an ungoverned hydro turbine. The generator is operated at maximum output power point corresponding to the excitation capacitance connected across the Point of Common Coupling (PCC) of the IIG. The output power of the generator is always maintained constant. With a fixed capacitor bank connected across the stator terminals, the IIG simultaneously supplies power to both ac load (main load) and controlled dc load (dump load). This modified MPPT algorithm not only tracks the maximum output power point, but also diverts power from main load to dump load and vice-versa as per requirement to maintain the voltage and frequency at constant value. The electronic circuit is realized using an uncontrolled rectifier-chopper (diode-IGBT) combination connected across the IIG terminals supplying the controlled dc load. The IIG has been modeled based on stationary reference frame d-q axes theory. The proposed control algorithm has been experimentally implemented on a three-phase, 415 V, 2.2 kW induction machine using dSPACE 1104 platform and the effectiveness of the algorithm has been tested for various transient conditions, such as, sudden ac load application as well as removal.

A novel prognostic model of breast cancer based on cuproptosis-related lncRNAs

ObjectiveBreast cancer (BC) is a deadly form of malignancy responsible for the death of a large number of women every year. Cuproptosis is a newly discovered form of cell death that may have implications for the prognosis of BC. Long non‐coding RNAs (lncRNAs) have been shown to be involved in the progression and development of BC. Here within, a novel model capable of predicting the prognosis of patients with BC was established based on cuproptosis-related lncRNAs.MethodsData of breast cancer patients was downloaded, including clinical information from The Cancer Genome Atlas (TCGA) database and lncRNAs related to cuproptosis were isolated. In total, nine lncRNAs related to copper death were obtained by Cox regression model based on Least Absolute Shrinkage and Selector Operation (LASSO) algorithm for model construction. The model was verified by overall survival (OS), progression-free survival (PFS) and receiver operating characteristic (ROC) curve. The differences in immune function, tumor mutation burden (TMB) and tumor immune dysfunction and exclusion (TIDE) between patients with different risk scores were analyzed.ResultsBased on cuproptosis-related lncRNAs, a prognostic model for predicting BC was constructed. Each patient was assigned a risk score based on our model formula. We found that patients with higher risk scores had significantly lower OS and PFS, increased TMB, and higher sensitivity to immunotherapy.ConclusionsThe model established in this study based on cuproptosis-related lncRNAs may be capable of improving the OS of patients with BC.

DNA-methylome-derived epigenetic fingerprint as an immunophenotype indicator of durable clinical immunotherapeutic benefits in head and neck squamous cell carcinoma.

Cancer immunotherapy provides durable response and improves survival in a subset of head and neck squamous cell carcinoma (HNSC) patients, which may due to discriminative tumor microenvironment (TME). Epigenetic regulations play critical roles in HNSC tumorigenesis, progression, and activation of functional immune cells. This study aims to identify an epigenetic signature as an immunophenotype indicator of durable clinical immunotherapeutic benefits in HNSC patients. Unsupervised consensus clustering approach was applied to distinguish immunophenotypes based on five immune signatures in The Cancer Genome Atlas (TCGA) HNSC cohort. Two immunophenotypes (immune 'Hot' and immune 'Cold') that had different TME features, diverse prognosis, and distinct DNA methylation patterns were recognized. Immunophenotype-related methylated signatures (IPMS) were identified by the least absolute shrinkage and selector operation algorithm. Additionally, the IPMS score by deconvolution algorithm was constructed as an immunophenotype classifier to predict clinical outcomes and immunotherapeutic response. The 'Hot' HNSC immunophenotype had higher immunoactivity and better overall survival (p = 0.00055) compared to the 'Cold' tumors. The immunophenotypes had distinct DNA methylation patterns, which was closely associated with HNSC tumorigenesis and functional immune cell infiltration. 311 immunophenotype-related methylated CpG sites (IRMCs) was identified from TCGA-HNSC dataset. IPMS score model achieved a strong clinical predictive performance for classifying immunophenotypes. The area under the curve value (AUC) of the IPMS score model reached 85.9% and 89.8% in TCGA train and test datasets, respectively, and robustness was verified in five HNSC validation datasets. It was also validated as an immunophenotype classifier for predicting durable clinical benefits (DCB) in lung cancer patients who received anti-PD-1/PD-L1 immunotherapy (p = 0.017) and TCGA-SKCM patients who received distinct immunotherapy (p = 0.033). This study systematically analyzed DNA methylation patterns in distinct immunophenotypes to identify IPMS with clinical prognostic potential for personalized epigenetic anticancer approaches in HNSC patients. The IPMS score model may serve as a reliable epigenome prognostic tool for clinical immunophenotyping to guide immunotherapeutic strategies in HNSC.

Research on risk prediction algorithm for operation and maintenance data based on grey theory

This paper proposes a risk prediction algorithm for power operation and maintenance data networks based on the grey theory, aiming at the real-time, healthy, and high stability requirements of power operation and maintenance data networks in work. By combining the entropy weight method to assign weight to risk indicators, the risk prediction of data networks in dynamic networks is achieved. By using two indicators of risk value and risk dispersion, the overall system is predicted for risk. Through experimental comparison, the prediction algorithm based on grey theory designed in this paper has a smaller error than existing prediction methods, which more scientifically realizes the risk prediction mechanism, ensures the stability of power operation and maintenance data, and ensures the reliable operation of the power grid.

Research and application of target detection algorithm for live operation in substation

The substation is an important node connecting the backbone network and distribution network and is the core hub of the smart grid. With the continuous increase of electrical equipment, the substation plays a vital role in the whole power system. The structural principle of deep learning convolutional neural network is deeply studied, which lays a theoretical foundation for substation fault detection and recognition based on the object detection algorithm. This paper focuses on fault detection algorithms based on deep learning. First, two mainstream target detection algorithms, Faster R-CNN and YOLO V5, are applied to the self-made substation fault data set. The experimental comparison shows that while the YOLO V5 algorithm has a slightly higher recognition speed, the accuracy of fault target recognition and similar background image recognition is higher than that of the Faster R-CNN algorithm. Then, for the benchmark network YOLO V5, an improved method is proposed. The innovative idea is to delete large branches from the network detection part for the detection of small faults in the station, so as to reduce the number of model parameters and improve the speed of model detection.

Development of intelligent protection and automation control systems using fuzzy logic elements

In this article, the causes of technological disturbances in electrical systems are considered, and several characteristic disadvantages of the protection and automation of elements of electrical systems are highlighted. The tendency to decrease the reliability of relay protection associated with the transition from analog to digital types of protection is substantiated. Based on the studied examples, the use of fuzzy logic in protections, the expediency of using fuzzy logic elements in protection devices, and the automation of electrical systems to identify types of short circuits are justified. This article analyzes the most common damages and presents the results of modeling an electrical system with transformer coupling, where all types of asymmetric short circuits were initiated. The dynamics of changes in the symmetrical components of short-circuit currents of the forward, reverse, and zero sequences are determined. Rules have been created for the identification of asymmetric types of short circuits. An algorithm of protection and automation operation using fuzzy logic elements has been developed. The proposed algorithm of protection and automation will reduce the time to determine the type of damage and trigger protections.

MR-SFAMA-Q: A MAC Protocol based on Q-Learning for Underwater Acoustic Sensor Networks

<p>In recent years, with the rapid development of science and technology, many new technologies have made people’s exploration of the ocean deeper and deeper, and due to the requirements of national defense and marine development, the underwater acoustic sensor network (UASN) has been paid more and more attention. Nevertheless, the underwater acoustic channel has the properties of considerable propagation delay, limited bandwidth, and unstable network topology. In order to improve the performance of the medium access control (MAC) protocol in UASN, we propose a new MAC protocol based on the Slotted-FAMA of Multiple Reception (MR-SFAMA) protocol. The protocol uses the Q-Learning algorithm to optimize the multi-receiver handshake mechanism. The current state is judged according to the received node request, and the Q-table is established. Through the multi-round interaction between the node and the environment, the Q-table is continuously updated to obtain the optimal strategy and determine the optimal data transmission scheduling scheme. The reward function is set according to the total back-off time and frame error rate, which can reduce the packet loss rate during network data transmission while reducing the delay. In addition, the matching asynchronous operation and uniform random back-off algorithm are used to solve the problem of long channel idle time and low channel utilization. This new protocol can be well applied to unstable network topology. The simulation results show that the protocol performs better than Slotted-FAMA and MR-SFAMA regarding delay and normalized throughput.</p> <p> </p>

Retracted: Risk Prediction Algorithm of Social Security Fund Operation Based on RBF Neural Network

Retracted: Risk Prediction Algorithm of Social Security Fund Operation Based on RBF Neural Network

Emergency Prevention Control as a Means of Power Quality Improvement in a Shipboard Hybrid Electric Power System

The problems associated with the greenhouse effect have increased the desire to limit carbon dioxide (CO2) emissions into the atmosphere, including emissions produced by shipboard electrical power systems. This has led to a further search for solutions in this area, such as shipboard hybrid electric power systems (SHEPSs). These systems do not yet have a wide application compared with traditional shipboard electrical power systems for several reasons, including the lack of studies establishing the fault tolerance of such systems. Therefore, in this work, problems related to generated power quality deterioration under large disturbances are studied. To achieve the fault-free operation of SHEPS, an emergency prevention control (EPC) system based on controlled parameter forecasting, along with a system structure and operation algorithm, is developed. The goals of improving electrical power quality are achieved by increasing the control efficiency of the power system’s generating sources. To validate the feasibility of the proposed control system, a computer simulation was carried out after developing a mathematical model of the SHEPS under study. The results of the study show that the use of the proposed EPC system will improve power quality when the controlled parameters are within acceptable limits. At the same time, further research is needed, as the problem of false control action as a consequence of EPC system hardware or software faults remains unstudied.

Realization of ultracapacitor as sole energy storage device in induction motor drive electric vehicle with modified state timing based field weakening control algorithm

This article employs the concept of realizing an electric vehicle (EV) driven by an induction motor (IM) with an ultracapacitor (UC) as a sole energy storage device for a short distance range in city drive. In battery-driven EVs, the performance of batteries will extensively degrade during frequent start, stop, acceleration and deceleration of the vehicle. However, UC can withstand that circumstance without any degradation. High charge/discharge efficiency, high power density, high temperature withstand capabilities and quick charging capability allows UC to run the EV for an extended range, including the regenerative braking technique. Apart from source, EVs also require efficient drive control for a smooth vehicle operation in the entire driving range. To achieve the vehicle characteristics, it is preferred to operate the propulsion drive in the field weakening (FW) region over the constant torque region. This paper also proposes a Modified State Timing SVPWM Based Control Algorithm for the FW region operation of IM for EV applications. The proposed algorithm is designed to overcome the torque ripple issue of the conventional SVPWM Based Control Algorithm in the FW region. The reduction in torque ripple is achieved by imposing current and voltage limits on the motor d and q axis by considering a maximum modulation index of 1.2732 and with an additional current loop for mitigating the transients in the torque due to sudden variation of the load. The simulation/hardware studies show a smooth operation of UC-driven EV for an entire driving range with a 78.57 percent improvement in torque ripple in the FW region by the proposed algorithm compared with the conventional FW control algorithm. The real time Lab prototype of UC driven EV has been developed and the proposed algorithm has been validated in real-time using the STM32F407VGTx ARM Cortex M4 microcontroller.

A New Link Adaptation Technique for Very High Frequency Data Exchange System in Future Maritime Communication

The growing demand for communication technology capable of providing high transmission rates in maritime environments has led to the exploration of the very high frequency (VHF) data exchange system (VDES) as a promising solution. The VDES, the integration of an automatic identification system (AIS), application-specific messaging (ASM), and VHF data exchange (VDE), offers improved transmission rates and stable connections compared with existing technologies. Although the VDES supports high transmission rates through various modulation and coding scheme (MCS) technologies, it lacks a standardized mechanism for controlling MCS parameters and relies on user algorithms for operation. In this paper, we introduce the maritime auto-rate fall-back (mARF) technology, designed to effectively address the challenges of maritime communication scenarios using the MCS framework provided by the VDES. mARF technology incorporates fast drop-out and recovery mechanisms to swiftly adapt to changing MCS types in the presence of deep nulls, a common occurrence in maritime communication environments. These adaptive thresholds for fast drop-out and recovery operations are dynamically learned using historical communication data. Through extensive simulations, we demonstrate the effectiveness of mARF in enhancing the MCS control capabilities of the VDES. Our results show a significant performance improvement of 18% compared to the existing model, validating the potential of mARF in optimizing maritime communication channels and supporting a high transmission rate.

Practical and privacy-preserving geo-social-based POI recommendation

With the rapid development of location-based services and online social networks, POI recommendation services considering geographic and social factors have received extensive attention. Meanwhile, the vigorous development of cloud computing has prompted service providers to outsource data to the cloud to provide POI recommendation services. However, there is a degree of distrust of the cloud by service providers. To protect digital assets, service providers encrypt data before outsourcing it. However, encryption reduces data availability, making it more challenging to provide POI recommendation services in outsourcing scenarios. Some privacy-preserving schemes for geo-social-based POI recommendation have been presented, but they have some limitations in supporting group query, considering both geographic and social factors, and query accuracy, making these schemes impractical. To solve this issue, we propose two practical and privacy-preserving geo-social-based POI recommendation schemes for single user and group users, which are named GSPR-S and GSPR-G. Specifically, we first utilize the quad tree to organize geographic data and the MinHash method to index social data. Then, we apply BGV fully homomorphic encryption to design some private algorithms, including a private max/min operation algorithm, a private rectangular set operation algorithm, and a private rectangular overlapping detection algorithm. After that, we use these algorithms as building blocks in our schemes for efficiency improvement. According to security analysis, our schemes are proven to be secure against the honest-but-curious cloud servers, and experimental results show that our schemes have good performance.