Operation Planning Research Articles

SCADA Data-Driven Wind Turbine Main Bearing Fault Prognosis Based on One-Class Support Vector Machines

This work proposes a fault prognosis methodology to predict the main bearing fault several months in advance and let turbine operators plan ahead. Reducing downtime is of paramount importance in wind energy industry to address its energy loss impact. The main advantages of the proposed methodology are the following ones. It is an unsupervised approach, thus it does not require faulty data to be trained; ii) it is based only on exogenous data and one representative temperature close to the subsystem to diagnose, thus avoiding data contamination; iii) it accomplishes the prognosis (various months in advance) of the main bearing fault; and iv) the validity and performance of the established methodology is demonstrated on a real underproduction wind turbine.

Understanding the intra-day and intra-week ridership patterns of urban rail transit stations in London using a fuzzy clustering approach

The needs for transit station classification are ever-growing as the planning process, be it at a strategic or operational level, becomes increasingly automated, data-oriented, and short-cycled. Whilst most existing models have used binary methods, this study applied a fuzzy clustering approach and examined cluster memberships (i.e., to what degree a station belongs to each cluster) of London rail transit stations by using entry and exit data with intra-day and intra-week variations. A method of hyperparameter selection in fuzzy clustering considering the context of transportation and a framework of ridership variation analysis was proposed. The results suggest that fuzzy clustering can maximise the information from high-resolution temporal passenger flow data of urban rail transit. The membership breakdowns allow users to have a better understanding of station characteristics and help to avoid inadequate plans by treating the stations belonging to multiple clusters in a different manner from the binary clustering, where each station only belongs to one cluster. Furthermore, fuzzy clustering can capture the ridership variation patterns and reveal special clusters. The results can be potentially applied in operation planning, such as service timetabling, station staff working-hour designs and fare strategy designs, etc.

An Intelligent Obstacle Avoidance Method for Six Degree of Freedom Robotic Arm Based on Assembly Sequence Planning

Abstract Autonomous operation planning of robotic arms under complex task constraints is still a difficult problem in the current research in this direction, and the existing operation planning methods are facing challenges and urgently need to break through the technical bottleneck. This paper proposes an improved RRT algorithm for six-degree-of-freedom robotic arms, which can be used for obstacle avoidance path planning. Based on the assembly sequence planning method, combining the advantages of RRT and RRT-connect algorithm, applying the idea of target sampling to strengthen the convergence of the algorithm to the target point search, introducing the greedy algorithm to improve the efficiency of the algorithm, combining with the five times of the B-spline interpolation on the path of the smoothing optimization, to shorten the planning path time and length. The Robotics Toolbox toolkit in MATLAB is used to count the time, number of samples, and path length required for the planning of RRT and the improved RRT algorithm, and the dynamic planning library is used to configure the algorithm and verify the path planning effect of the improved algorithm. Compared with the traditional RRT algorithm, using the proposed SDPS-RRT Connect algorithm for six-degree-of-freedom robotic arm path planning, the minimum distance of the connecting rod from the obstacle is 51.4948cm, which is greater than the standard safety distance of 45cm, which verifies the feasibility of the algorithm in practical applications.

DTI fiber-tracking parameters adjacent to gliomas: the role of tract irregularity value in operative planning, resection, and outcome

DTI fiber-tracking parameters adjacent to gliomas: the role of tract irregularity value in operative planning, resection, and outcome

ПРИМЕНЕНИЕ МЕТОДА ПРОГНОЗИРОВАНИЯ СОСТОЯНИЯ МАССИВА В СЛОЖНЫХ ГОРНО-ГЕОЛОГИЧЕСКИХ УСЛОВИЯХ

The existing hydrogeological, instrumental and analytical methods of determining the location of the upper boundary of the zone of technogenic water-conducting cracks do not allow to predict its change in time in complex mining and geological conditions and taking into account the high rates of solid minerals extraction. Their application involves high risks of technogenic accidents. The developed method of predicting the condition of the rock massif on the basis of regularities of distribution of deformations in the developed massif on the example of solid minerals deposit is considered in the paper. The regularities obtained from the results of processing of field measurements are adapted and make it possible to forecast the development of geomechanical parameters taking into account the approved plans of mining operations, which provides the safety of the mine.

РЕКОМЕНДАЦІЇ ЩОДО УДОСКОНАЛЕННЯ СИСТЕМИ ОРГАНІЗАЦІЇ ОБЛІКУ ВИКОРИСТАННЯ АВТОМОБІЛЬНОЇ ТЕХНІКИ У ВІЙСЬКОВИХ ЧАСТИНАХ І З’ЄДНАННЯХ НАЦІОНАЛЬНОЇ ГВАРДІЇ УКРАЇНИ

The article shows the need to develop recommendations for improving the system of accounting for the use of motor vehicles in units, military units and formations of the National Guard of Ukraine. The solution of the tasks assigned to the National Guard of Ukraine is impossible without the widespread use of motor vehicles. A significant volume of necessary transportations poses tasks for the NGU servicemen related to increasing the efficiency of the use of motor vehicles. The Order of the Commander of the National Guard of Ukraine No. 900 of 27.12.2016 states: "the purpose of motor vehicle support is to achieve maximum efficiency of the use of motor vehicles...". The task of increasing the efficiency of the use of automotive equipment and ensuring a given level of efficiency of its use can be solved by comparing the efficiency indicators of the use of machines. To assess the operation and technical condition of motor vehicles, special indicators are used, such as fleet utilisation rate; working time utilisation rate; mileage utilisation rate; load capacity utilisation rate; speed and others. However, these indicators, neither individually nor in combination, make it possible to assess the effectiveness of the use of motor vehicles of a unit or military unit of the NGU. Therefore, there is a need to obtain a dependence for assessing the efficiency of transportation by military vehicles. It is proposed to use as an indicator of the efficiency of the use of motor vehicles an indicator that allows assessing the level of efficiency of the use of motor vehicles (when using motor vehicles in accordance with the annual plan for the operation and repair of vehicles of a military unit), to determine the impact on the efficiency of of transportations of certain indicators (coefficient of efficiency of transportations) and outline ways to ensure a given level of efficiency of the use of motor vehicles of the National Guard of Ukraine. The proposals for improving the structure of the vehicle operation record card and the summary of vehicle operation allow to improve the accounting of vehicle operation, improve the planning of vehicle operation, analyse and evaluate the results of their use.

Daily flow discharge prediction using integrated methodology based on LSTM models: Case study in Brahmani-Baitarani basin

For flood control, hydropower operation, and agricultural planning, among other applications, flow discharge prediction is a critical first step toward the strong and dependable planning and management of water resources. Floods are destructive natural calamities that destroy human lives and infrastructure across the world. Development of effective flood forecasting and prediction models is critical for minimising deaths and mitigating damages. This study employs hybrid deep learning Long Short Term Memory (LSTM) algorithms like LSTM, Convolution LSTM (Conv-LSTM) and Convolutional Neural Network LSTM (CNN-LSTM) to predict likelihood flood events using daily precipitation, daily temperature and daily relative humidity from two flood-forecasting stations i.e., Champua (Baitarani River, Odisha) and Jarikela (Brahmani River, Odisha) over a 20-year period. The results show that CNN-LSTM performed best followed by Conv-LSTM and LSTM in terms of R2 = 0.98055, 0.96564, and 0.93244, RMSE = 19.137, 35.635, and 49.347, MAE = 18.372, 33.766, and 47.058, NSE = 0.971, 0.9517 and 0.9257 respectively. The findings support the claim that machine learning models and algorithms, in particular CNN-LSTM model, can be applied to flood forecasting with high accuracy, thereby enhancing water and hazard management.

Planning of operation of technical systems of complex objects when using flexible strategies on the set of states of functioning

Verbal and formal models and methods of solving the problem of optimal planning of operation of technical systems of complex objects using flexible strategies on the set of states of functioning are presented. Technical systems fulfill the functions of providing the required conditions of functioning of technological equipment of complex objects. In complex multilevel objects technical systems occupy the lowest hierarchical level, which imposes some restrictions on the development of long-term plans for their operation, the timing and volume of preventive maintenance. Taking into account the differences and peculiarities of technical systems, we have developed mathematical models of quality indicators of the elements' functioning taking into account the implementation of preventive measures, and set the task of optimal long-term operation planning at the verbal and formal levels. The coefficients of readiness and operational readiness of system elements are taken as indicators of optimality under the constraints of specified terms, costs and duration of preventive maintenance. When planning control actions on the technical state of systems, not only the scope of preventive maintenance, but also the degree of utilization of redundant elements for their intended purpose are determined. To solve the optimization problem, an algorithm based on the random search method is developed. As a result of the performed research the following results are obtained: - mathematical models of complex indicators of reliability of elements of technical systems; - mathematical model of long-term planning of operation of technical systems; - an algorithm for finding an optimal solution based on the random search method.

MATH SOFTWARE FOR OPTIMIZATION PROBLEM OF MULTI-PERIOD MIXING SEQUENCES FOR REFINERY OPERATING PLAN DEVELOPING

The purpose of this work is to use mathematical modeling methods and develop software for optimizing a pre-calculated operating plan in order to minimize the deviation in the production of components of mixing sequences from the integrated values of the refinery schedule.

Development of Work Breakdown Structure (WBS) for Rapid Damage Runway Repair (RADARR) Time Planning at Military Airbases

The military airbase plays a crucial role in national defense, particularly in air defense. One of the most critical facilities at a military airbase for supporting air operations is the runway that serves as the area where fighter aircraft take off and land; hence. If such an event occurs, the Air Force’s capability, specifically the ability of fighter aircraft to take off and land, would be compromised, disrupting air operations. Therefore, the rapid damage runway repair (RADARR) method, aligned with the rigid pavement construction approach, is essential. The foundation of the construction implementation involves creating a Work Breakdown Structure (WBS) as a reference for RADARR operation planning. This study aims to develop a WBS as the basis for RADARR operation time planning. The research method employed is a mixed-method approach with descriptive analysis. The variables under investigation include work packages, construction methods, work activities, and the duration of operations. This study comprises expert judgment, pilot surveys, respondent surveys, and focus group discussion (FGD) followed by the development of RADARR WBS, measure the operational time of RADARR through respondent surveys. These findings serve as a valuable reference for executing RADARR construction work within the military context.

An integrated framework for UAV-based precision plant protection in complex terrain: the ACHAGA solution for multi-tea fields.

UAV-based plant protection represents an efficient, energy-saving agricultural technology with significant potential to enhance tea production. However, the complex terrain of hilly and mountainous tea fields, coupled with the limited endurance of UAVs, presents substantial challenges for efficient route planning. This study introduces a novel methodological framework for UAV-based precision plant protection across multiple tea fields, addressing the difficulties in planning the shortest routes and optimal flights for UAVs constrained by their endurance. The framework employs a hyperbolic genetic annealing algorithm (ACHAGA) to optimize UAV plant protection routes with the objectives of minimizing flight distance, reducing the number of turns, and enhancing route stability. The method involves two primary steps: cluster partitioning and sortie allocation for multiple tea fields based on UAV range capabilities, followed by refining the UAV's flight path using a combination of hyperbolic genetic and simulated annealing algorithms with an adaptive temperature control mechanism. Simulation experiments and UAV route validation tests confirm the effectiveness of ACHAGA. The algorithm consistently identified optimal solutions within an average of 40 iterations, demonstrating robust global search capabilities and stability. It achieved an average reduction of 45.75 iterations and 1811.93 meters in the optimal route, with lower variation coefficients and extreme deviations across repeated simulations. ACHAGA significantly outperforms these algorithms, GA, GA-ACO, AFSA and BSO, which are also heuristic search strategies, in the multi-tea field route scheduling problem, reducing the optimal routes by 4904.82 m, 926.07 m, 3803.96 m and 800.11 m, respectively. Field tests revealed that ACHAGA reduced actual flight routes by 791.9 meters and 359.6 meters compared to manual and brainstorming-based planning methods, respectively. Additionally, the algorithm reduced flight scheduling distance and the number of turns by 11 compared to manual planning. This study provides a theoretical and technical foundation for managing large-scale tea plantations in challenging landscapes and serves as a reference for UAV precision operation planning in complex environments.

Context-aware early warning method for monitoring business flow anomalies in transmission grid construction based on context-awareness

Abstract Based on context-aware theory, this paper proposes an online anomaly detection and warning method for multi-angle transmission grid construction business processes. The business process model for transmission grid construction is constructed using a Petri net, and the data is preprocessed. The context in the business event log is initially refined, and the data in the model training is labeled using the supervised learning method. The sliding window Euclidean distance model is applied to build an anomaly detection model for the transmission grid construction business process, and the data is filtered by mean value aggregation to obtain the threshold value for anomaly monitoring and to realize anomaly detection and warning. This paper’s anomaly detection and early warning model is applied to carry out transmission grid construction business flow optimization practice in the G Power Supply Bureau of Yunnan Province, China. Since the optimization in June, the timeliness and accuracy of material matching of G Power Supply Bureau have been stable at more than 90%, and the execution rate of the operation plan and on-time completion rate of billing have also been maintained at a high level of about 95%. The number of cancellations and adjustments to transmission network construction operations has decreased from 20% to 30% to less than 7%, and the overall satisfaction score of transmission network construction business processes has reached 4.15.

Time Series and Machine Learning Hybrid Models for Food Condiment Demand Forecasting: A Case Study in Thailand

Food industry is one of the most important industries in Thailand. The case-study company is a condiment manufacturer that needs to efficiently manage and plan for their business. One of the most important issues is demand forecasting. The company should precisely forecast their product demands, which will be used for operation planning. This study proposes forecasting models for both short-term and long-term planning for the company’s main condiment products. The proposed models are time series, machine learning and hybrid forecasting models which will be compared with pure time series and machine learning methods. Unlike previous work, this study proposes an innovative hybrid model, i.e., Holt- Winters exponential smoothing and Seasonal Autoregressive Integrated Moving average hybrid with Artificial neural network, which has never been considered previously. The accuracy is measured by mean absolute percentage error (MAPE) where the results are also compared to the method currently used in the company. The results show hybrid forecasting model provides the lowest overall error for both short-term and long-term forecast. The most accurate model from this paper can provide MAPE of 2.07% from short-term forecast and MAPE of 2.20% for long-term forecast (6 months in advance). When comparing with the company’s existing MAPE of 20.05%, the proposed model can increase forecast accuracy effectively.

EDUCAÇÃO POPULAR EM SAÚDE: SIGNIFICADO DA EXPERIÊNCIA NA FORMAÇÃO DE AGENTES COMUNITÁRIOS DE SAÚDE

ABSTRACT: This study brings results from a qualitative research aiming to analyze the meaning of the training experience of Community Health Workers (ACSs, in Portuguese) in the Qualification Program in Popular Education in Health (EdPopSUS), one of the priority strategies of the Operative Plan of the National Policy of Popular Education for the Unified Health System, in Brazil. Data production occurred between 2018-2019 and included documentary analysis and semi-structured interviews with the ACSs and the health and EdPopSUS managers. The textual material produced was interpreted by content analysis, in light of the Popular Education theoretical framework. The results showed that EdPopSUS encouraged the ACSs' reflection, dialogue, and affection, enhancing their creativity and autonomy in an educational space of self-knowledge and interaction with their experienced world. ACSs' personal and professional experiences were valued, improving their communication with the team, the community, and the families, their listening skills, and their ability to conduct collective educational activities. The field trips and the conversation rounds were highlighted as potentialities of this training. Challenges were identified in implementing the principles of Popular Education in Primary Health Care. The experience analyzed strengthened ACSs' role as agents of change and popular educators. In this sense, Popular Education in Health was reinforced as a pedagogical-social practice, strengthening people, movements, teams, care practices, and the health system. The educational processes based on Popular Education are recommended for the permanent education of health teams.

Optimization of the Operation Plan of Airport Express Train with Consideration of Train Departure Time Window

This paper proposes an optimization model for the train operation scheme of the Airport Express Line (AEL) based on the expected arrival time of passengers by the introduction of the train departure time to cope with the time‐dependent passenger flow and provide better prompt train service according to passengers’ demand. Considering factors such as train sections, station arrangement, passenger capacity, departure time windows, passenger flow conservation, and boarding and disembarkation processes, this paper also aims to find the optimal combination of the passengers’ total travel time and the train operation cost. A set of alternative train options is introduced to simplify the model and convert integer variables related to train pairs into 0‐1 variables. The elaborately designed simulated annealing algorithm mainly focuses on the key elements of strategies like initial solution generation, neighborhood solution construction, and the allocation of passenger flows, tailored to the model’s unique features and the time‐dependent passenger flow. Neighborhood solution strategies include the increase or haut of train operations and the adjustment of the number of stops, which refines the solution space and boosts the process efficiency of the heuristic algorithm. Additionally, the model and algorithm proposed in this paper are practiced during the peak hour of Nanjing Metro Line S1 for empirical validation. The research findings demonstrate that the optimized train operation scheme is better synchronized with the fluctuating number of time‐dependent passenger flows and exhibits notable improvement in computational efficiency and convergence.

Improving the Connectivity of Mobile Networks Through the Use of Deep Learning for Planning Neighborhood Relations

This work proposes a Deep Learning approach to help mobile operators plan neighborhood relationships within their network in the form of an application allowing to map sites, sectors and their cells, to calculate the neighbors of different cells based on deep learning algorithms and integrate new sectors or sites on the network. By leveraging complex machine learning techniques, the tool developed can identify patterns, trends and hidden relationships between different base stations in the network. This leads to more effective and efficient planning of neighbor relationships, thereby reducing interference and improving quality of service for subscribers. Additionally, the tool offers increased flexibility and adaptability. It is able to adjust to rapid network changes, such as adding new base stations or changing a network site's settings. This ability to adapt guarantees continuous and optimal planning of neighborhood relations, even in a dynamic and evolving environment.

Functional Outcome of Distal Femoral Fractures using NEER Scoring Managed by Distal Femoral Locking Plate versus Retrograde Intramedullary Nail: A Cross-sectional Study

Introduction: Distal femur fractures are a group of fractures that have been a topic of debate for the past decade, were traditionally treated mainly using conservative methods. However, with advancements in surgical techniques and implants, operative intervention has now become the preferred treatment approach. There are various methods for treating distal femur fractures and the present study compares the treatment of distal femoral fracture using distal femur nailing versus the use of locking plate. Aim: To compare the functional outcomes of stabilising distal femur fractures using locking compression plating versus retrograde nailing and to evaluate the merits and drawbacks of each procedure. Materials and Methods: A cross-sectional comparative study was conducted in the Department of Orthopaedics at Calcutta National Medical College and Hospital, Kolkata, West Bengal, India, over a one-year period from (January 2021 to January 2022. Total 46 patients with distal femur fractures were included, with six lost to follow-up. Two groups were divided in which total 20 patients underwent distal femoral nailing, while 20 were treated with distal femoral locking plates. Functional outcomes were assessed using the NEER Score, aiming for perfect restoration of the articular surface, stable fixation, full knee range of motion, and good functional results. Data were entered into a Microsoft Excel spreadsheet for statistical analysis using Statistical Packages for Social Sciences (SPSS) (version 27.0; SPSS Inc., Chicago, IL, USA). Numerical variables were summarised as mean and standard deviation, while categorical variables were presented as counts and percentages. A p-value of ≤0.05 was considered statistically significant. Results: The study population consisted of 70% males with a mean age of approximately 55 years. Road traffic accidents were the most common mode of injury. The mean time for radiological union in the nailing group was 14.05 weeks, significantly shorter than the 16 weeks in the plating group. The mean range of knee flexion was 111° in the nailing group and 114.5° in the plating group. The average NEER score was 84.10 for the plating group and 83.30 for the nailing group. There was no statistically significant difference in functional outcomes between the two groups. Conclusion: Both retrograde intramedullary nailing and the use of locking compression plates can be considered as suitable treatment options. Early weight-bearing can be initiated with retrograde nailing. No significant difference was observed in terms of outcome, fracture healing, or NEER score. The time taken for radiological union of fractures using both techniques was comparable. Proper operative planning, execution, and adherence to a strict rehabilitation protocol play crucial roles in the overall patient outcome.

Econometric modeling of carbon price impacts on the bidding mechanism between different energy sources in the electricity market

Abstract Virtual power plants (VPPs) can aggregate different energy sources into a whole to participate in the electricity market and carbon trading market, and as the scale of VPPs continues to expand, they will also be transformed from the original price takers to price makers. The paper considers VPP as a price setter and suggests a two-tier bidding strategy for the VPP day-ahead electricity market that incorporates the impact of the carbon price. Among them, the upper-layer model aims to maximize the expected revenue of VPP, while the lower-layer model aims to minimize the system clearing cost. The dynamic carbon emission factor is used in the strategy to create a market incentive factor that corrects the offer price of each unit before clearing it again. Finally, the optimal bidding strategy, as well as the operation plan of the VPP, are given through an arithmetic example, and the impact of the uncertainty in the wind farm output forecast in the VPP on the expected return of the VPP is analyzed. The impact of carbon prices on power prices, output, and rates of energy demand change are further explored to provide a basis for the compensation mechanism for ecological protection of carbon values. The arithmetic example shows that VPP can influence the market price through strategic bidding decisions, and after considering the carbon price, the power market clearing price is 4.46 yuan/MWh, and the maximum gain is obtained as 225.54. The consumption rate of new energy units such as WT and PV is increased, the carbon emissions are reduced, and the total gain of VPP is increased. The increase in carbon prices and the offer of traditional generators increase the amount of electricity cleared by renewable energy units, while the increase in carbon quota ratio and the offer of new energy generators increase the average clearing price.

Reverse oblique fracture needs more augmentation with IM nail: A use of trochanteric support plate (TSP)

Abstract Background: There are various type of unstable intertrochanteric femur fracture of which reverse oblique fracture is most challenging to fix because of its displacement. Reverse oblique fracture accounts for 1/3rd of all hip fractures and are very unstable. Posteromedial part is effectively support by intramedullary nail but fail to support broken lateral wall. Objective: A Prospective study to evaluate the comparative outcome of buttressing lateral wall with trochanteric support plate(Long PFN + TSP) & without trochanteric support plate(long PFN alone). Materials and Methods: A study was carried out from 2018 to 2023; 60 cases of acute reverse oblique intertrochanteric femur fracture were studied, out of which 28 were treated with long PFN +TSP plate & 32 were treated with Long PFN. Result: Bone healing is observed in all patient. Minimum average follow up of patients was 10.2 months. Patient treated with only Long PFN have mean bone healing time of 116.06 days whereas patients treated with Long PFN + TSP plate have mean time of 61.56 days. Conclusion: With proper pre operative planning, with anatomical/acceptable reduction long PFN + TSP plate is an ideal modality for reverse oblique intertrochanteric femur fracture.

Design and Evaluation of Field Segmentation, Path Generation and Sequential Point Tracking Algorithms for Agricultural Mobile Robots

Highlights Field segmentation, path generation, and sequential point tracking algorithms were designed for agricultural mobile robots. An optimized field segmentation plan and operation path could be generated for the robot. The sequential point tracking algorithm could guide the robot to a series of target points. The algorithms were tested in simulation calculations and practical experiments. Abstract. As agricultural production becomes increasingly intelligent, operations by autonomous mobile robots become an inevitable trend. For some operations, such as in-field soil sampling, agricultural robots need to track a series of target points in the field. In this work, a field segmentation algorithm, a path generation algorithm, and a sequential point tracking algorithm were designed and evaluated. The field segmentation and operation path generation algorithms, which took, respectively, the total lost area of the field and the total turning angle of the robot as optimization objectives, aimed to provide an efficient operation plan for the robot to follow. The sequential point tracking algorithm was designed for the robot to realize automatic tracking of the planned target points following a desired sequence. The algorithms were tested by simulation calculations using the boundary coordinates of two test fields and field experiments based on a small four-wheeled mobile robot. Field results of the straight-path, multi-target point tracking experiment showed that the designed steering angle and velocity control laws could accurately guide the robot from a start point to a series of target points when the robot was running on a concrete surface. The absolute distance errors of the robot with respect to the targets were smaller than 0.03 m. To test the robot’s tracking performance on different surface conditions, two whole-field, continuous point tracking experiments were performed. The general effectiveness of the sequential point tracking algorithm was validated. When the robot was running on hard surface conditions (concrete and grass), the tracking errors were between 0.014 m and 0.074 m. When the robot was running on a soft and bumpy soil surface, the tracking accuracy degraded to submeter level due to the limitation of small-size wheels. The average, RMS, and maximum errors were 0.774 m, 0.775 m, and 0.798 m, respectively. However, the feasibility of the sequential control algorithm was not influenced. The designed algorithms could be potentially utilized in the development of robotic systems for in-field site-specific operations. Keywords: Agricultural mobile robot, Control sequence, Field segmentation, Operation path generation, Point tracking.