The conventional electrical grid faces significant issues, which this paper aims to address one of most of them using a proposed prototype of a smart microgrid energy management system. In addition to relying too heavily on fossil fuels, electricity theft is another great issue. The proposed energy management system can simultaneously detect electricity theft and implement demand response tactics by employing time-of-use pricing principles and comparing real electricity consumption with grid data. The system uses the Al-Biruni earth radius (BER) optimization algorithm to make smart choices about how to distribute the load, intending to reduce energy consumption and costs without sacrificing comfort. As a bonus, it considers limitations imposed by battery charging/discharging and decentralized power generation. Incorporating sensors and SCADA-based monitoring, the system provides accurate measurement and management of energy usage through load monitoring and control. An intuitive mobile app also helps consumers connect, allowing for more active participation and better control over energy use. Extensive field testing of the prototype shows that by moving loads from peak period to another off-peak period, electricity expenditures can be reduced by up to 48.45%. The energy theft value was calculated to be 1199 W, proving that the system's theft detection model was effective. SMGs can improve the resilience and stability of the power supply, reduce fossil fuel use, and lower energy costs. Figure 2. Smart microgrids use modern control systems and algorithms to optimize the use of existing resources and respond to demand and supply changes in real-time3. SMGs have the following characteristics,Demand response management: SMGs can use advanced algorithms to adjust the power consumption of connected devices in response to changes in demand, helping to balance the supply and demand of power.Energy storage management: SMGs can use energy storage systems to store excess energy generated by renewable sources, and release it as needed to meet demand.Distributed energy resource management: SMGs can use advanced algorithms to optimize the operation of distributed energy resources, to ensure the most efficient use of available resources.Power quality management: SMGs can use advanced control systems to maintain a stable and reliable power supply, even during disturbances on the grid.Load balancing: SMGs use advanced algorithms to balance the load across different distributed energy resources and energy storage systems to ensure a stable, reliable power supply.Real-time monitoring: SMGs use sensors and monitoring systems to collect real-time data on the status of the grid, allowing operators to make informed decisions about how to manage the system.Smart microgrids (SMGs) are small, localized power grids that can work alone or alongside the main grid. A blend of renewable energy sources, energy storage, and smart control systems optimizes resource utilization and responds to demand and supply changes in real-time5. Smart microgrids can analyze sensor and meter data to identify trends of energy theft.Energy theft threatens the economic viability and sustainability of smart microgrids. Theft of energy includes tampering, bypassing, and unlawful connections. Energy theft, including smart microgrids, costs the global energy industry billions of dollars. The dispersed architecture and distributed energy supplies of smart microgrids make them more vulnerable to electricity theft than conventional power grids4.There are many strategies for energy management systems for smart microgrids such as load management, generation management, and energy storage management6. Critical loads in hospitals, nursing homes, and data centers are essential to running a facility and must never be interrupted. While discretionary loads can be temporarily cut off during peak times, emergency load sheds are needed to prevent blackouts. To reduce demand, heating, ventilation, and air conditioning systems, washing machines, and water heaters can be turned off during peak consumption. To prevent microgrid power disruptions, emergency loads in residential and commercial buildings must be shed. The authors of7 suggested using an intelligent load shedder module to isolate non-essential loads from the microgrid. They looked into ways to determine the priority and management of loads in different situations to improve efficiency. The classification, characterization, and tracking of loads necessitate systems for aligning loads with generation. Abdelsalam et al.8 suggested a hybrid demand-side management-multi-agent system energy management strategy for residential, commercial, and industrial microgrids. In9 the authors used the idea of electric springs in series to manage bus voltage and fault ride-through. They researched how to conserve electricity during situations of generating intermittent by reducing non-essential loads and maintaining critical loads. The authors of10 suggested using a combination of neural networks and fuzzy logic to create a system for managing energy demand, considering the significance of different types of loads, like residential, commercial, industrial, and hospital areas in an integrated energy management system (EMS). Additionally, they acknowledged that loads could also play a role in managing energy production in modern electrical systems.The control system of a microgrid must continuously analyze and prioritize loads to maintain a balance between power generation and consumption. Microgrid loads are usually critical or non-critical11. Since solid oxide fuel cells (SOFC) are more efficient, researchers suggested employing them as supplementary units in the SMG. The authors in12 put forth two distinct approaches, known as frequency analysis and the electrical model of the triple combined cycle, to address the power variations caused by utilizing large-scale PV modules in a microgrid. The triple combined cycle (TCC) based SOFC model incorporates both gas and steam turbines. They utilized the swing equation for the grid-connected frequency model of the SOFC-TCC and examined the power response characteristics in the electricity model. An optimum microgrid framework that parallelizes capacitive-coupling inverters (CCIs) and inductive-coupling inverters (ICIs) is presented in13. It offers adaptive power sharing for CCIs using lower DC-link voltages to reduce power and losses. Using a neural network-based control layer, simulations, and experiments show accurate and fast sharing. Lin et al.14 explores time-domain analysis for grid-connected inverters (GCIs) in asymmetrical grid situations. A phase-locked loop-based harmonic state space model is created. Eigenvalue analysis and simulations show how grid impedance asymmetry affects GCI stability, underlining the need for weak grid context-specific control techniques. Experimental validation proves the model's efficacy and theoretical findings. The Robust antlion optimizer algorithm tracks maximum power point in solar PV systems15. Charge controller design, DC-to-AC converter tuning, and power scheduling minimize operational costs and stabilize the grid in the study. The study helps stakeholders find sustainable and affordable energy alternatives. Bi-level planning for a cooling-heat-electric multi-energy coupled distribution network is presented in16. It increases PV consumption with a distributed hydrogen-thermal storage system through a multi-objective expansion planning model for network dependability.Since distributed resources comprise the bulk of SMG systems, they necessitate complex power control strategies to function normally. The benefits of generation control in hybrid SMGs and the grid integration of diverse distributed generation (DG) were presented byEnergy storage systems (ESS) are essential for microgrid systems because they store and distribute electrical power to stabilize load and renewable energy generation, improve power quality, and ensure system reliability. ESSs are classified by storage and response as electrical, mechanical, chemical, electrochemical, or thermal.17. They can balance energy supply and demand, smooth renewable energy generating swings, and provide backup power during outages. Advanced control algorithms and communication systems are two of the technologies employed in SMGs to manage energy storage. Real-time monitoring and control of ESSs in microgrids can be enabled by integrating smart meters and other monitoring and control devices. The authors in18 proposed an idea for a mixed-mode EMS that can efficiently manage a microgrid by utilizing low-cost energy sources and determining the best energy storage option from an economic standpoint. This approach utilizes three distinct techniques: linear programming (LP) to handle economic power-sharing and continuous operation and LP and mixed integer linear programming (MILP) methods to handle the on/off utility mode operation. The particle swarm optimization method is used to compute the optimal positioning and size of ESS devices in order to minimize operational costs within the microgrid. However, if the depth of discharge of the battery is too high, the battery may degrade quickly and negatively impact the overall durability of the ESS. Tenfen et al.19 presented a MILP approach for optimal EMS using demand response (DR) and a penalty cost function, where the network investigated is a single-phase version of an AC network. However, the expense of battery degeneration owing to the increased depth of discharge is disregarded. The authors in20 addressed the issue of efficient battery energy storage and control in intelligent residential microgrid systems by designing a new adaptive dynamic programming algorithm. This algorithm uses a hybrid iterative approach with two iterations, known as the P-and V-iteration. The V-iteration minimizes cost within a given period, while the P-iteration improves the iterative value function. Through these iterations, the iterative value function is not guaranteed to increase monotonously and may converge to the optimal solution. In21, notable works on microgrid voltage regulation for supplying complex power loads have been highlighted, and then a filtration-based model predictive control method was implemented to evaluate the performance of ESS. The pinch analysis method was used in22 for conducting a comparative analysis of the ESS.Using microgrids, management of energy storage devices like batteries and flywheels in SMGs. Optimization of stored energy improves microgrid efficiency and dependability23, considering several factors such as the state of charge (SOC) speed balancing method, droop factor, voltage control, and current limit. The authors in24 improved controller gain with an interactive teaching–learning optimizer after proposing a robust fractional order controller for supercapacitor ESS to reduce control error and costs. Different control strategies of variable-speed pumped hydro storage were investigated in25 to determine the most effective approach at each level of the control hierarchy. The work in26 examined the efficiency during the under-voltage condition; and reported increased hydrogen production and considerable reductions in power fluctuations. Optimization approaches can increase the utilization of renewable energy sources, minimize the usage of fossil fuels, reduce energy expenditures, and improve the stability and reliability of the microgrid27.A droop control strategy was proposed inThis paper proposes a practical solution to improve the efficiency and security of energy management in smart microgrids. This paper presents a prototype of an intelligent microgrid energy management system. In order to optimize energy consumption and reduce costs, the system considers the uncertainty of renewable energy sources and the possibility of energy theft. The prototype employs machine learning algorithms and sensors to monitor and predict energy production and consumption and detect any unauthorized energy usage.The proposed method accounts for the Earth's radius, a crucial factor in accurately forecasting energy distribution across various locations. This consideration is necessary for optimizing the microgrid's load distribution to improve energy efficiency while also accounting for the unique geographical features of the area it serves.The incorporation of time-of-use pricing principles as part of the energy management system represents an innovative way to incentivize efficient energy consumption patterns among consumers.The use of the BER optimization algorithm in the proposed energy management system can lead to improved energy efficiency and cost savings.Developing an intuitive mobile app that empowers consumers to participate in and control their energy usage actively is a significant innovation. This enhances user engagement and promotes more sustainable energy practices.The paper's overarching novelty lies in its comprehensive approach to improving the dependability and sustainability of smart microgrid energy management. Addressing electricity theft issues and optimizing energy usage contributes to a more resilient and efficient energy infrastructure.The main contributions of this article are concluded in the following points:The rest of the article is organized into several sections: “The monitoring system constantly analyzes its current state and makes necessary adjustments to guarantee the SMG equipment always functions smoothly and safely. The combination of software and hardware in this SMG guarantees the system's continuous, real-time operation.28:IoT devices can measure and track the amount of energy the SMG generates and consumes.IoT monitoring can detect and diagnose microgrid issues.IoT monitoring can improve grid stability and dependability by integrating renewable energy sources like solar and wind into SMGs, enhancing resilience.IoT devices enable remote monitoring and control of the SMG, especially in inaccessible regions.IoT provides real-time data and insights into the performance and operation of SMGs. This enables more efficient control of the microgrid and improves its performance and dependability. IoT monitors microgrids in several ways29. They looked at how IoT protocols might affect the time-sensitive needs of smart grid operations. The authors of30 built an IoT-based remote energy monitoring device for smart grid and household energy management, optimization, and conservation. The device efficiently tracks residential energy usage, reduces energy waste, and reveals energy behaviors. To standardize and unify structure parts, the authors of31 proposed an IoT-based EMS framework. Intelligent energy management and smart building modes allow for formulating relevant guidelines. A discussion of real-time microgrid monitoring was presented by32. The authors use web servers’ software, and data was collected wirelessly and transferred using an Arduino embedded system and ethernet network module. The authors of4 proposed an IoT-based SMG control and demand side management solution using genetic algorithm optimization. The control technique improves the voltage and frequency of the SMG during system load operations.IoT security solutions must consider real-time details, according to the authors of33. It can also collect microgrid performance data including energy generation and consumption and provide operators with real-time SMG status updates. This enables efficient and accurate SMG monitoring and control, ensuring maximal performance34.As shown in Fig. 35. Managing critical infrastructure and industrial systems is another SCADA benefit. SCADA systems monitor and control important infrastructure including power plants, water treatment facilities, and industrial systems like manufacturing and oil and gas facilities. SCADA provides the reliability and security these systems need. If equipment fails or other issues arise, SCADA systems frequently feature redundancy and fail-safe mechanisms to keep them running. IoT devices are cheaper and simpler and can be utilized for more general monitoring and control than SCADA systems. IoT devices are quicker to connect to the internet and accessible from anywhere36.SCADA systems can process and analyze massive volumes of data in real-time and perform complex control operations because they have more powerful processing capabilities than IoT devicesThe proposed algorithm's primary goal is the minimization of the overall cost Subjected to the following constraints.4:Minimal appliance wait time and optimal electricity cost are the goals of the design process behind user comfort Subjected to the following constraints.As a rule of thumb, a 4-h window of delay per day for appliances is allowed. It's important to note that these 4 h were selected on purpose at peak times to draw attention to the longest device wait times. In the event of a power outage lasting longer than 4 h, the utility company is obligated to compensate its consumers by either refunding their money or reducing their electricity rates. An appropriate optimization method that addresses this issue is called BER optimization. Among its many benefits are its simplicity, global optimization, applicability to noisy settings, and scalability in terms of both the number of possible solutions and the amount of data needed to evaluate them. Additionally, it is probabilistic, solves effectively for mixed discrete/continuous problems, employs reward (objective function) information, and allows for multi-objective optimization. Figure 37. Recently, this optimization algorithm has been developed by38. Al-Biruni performed the calculation that determined the radius of the Earth in the eleventh century. He did this by standing on a hilltop and measuring the distance between the horizon and the ground. Al-Biruni took two separate readings. The first thing he did was measure the height of the peak. He took the angle to the mountain's crest twice, each time measuring it from a different vantage point. Using the following equation, which is depicted in Fig. BER optimization is a metaheuristic algorithm that is based on the premise of recreating the process of measuring the radius of the Earth utilizing the method devised by the Persian scholar Abu Rayhan Al-Biruni in the eleventh century. This approach was used to measure the radius of the EarthAl-Biruni climbed to the top of the mountain and determined the angle of the horizon. The following equation, applied to his observations, allowed him to determine Earth's radius as depicted in Fig. The cooperative optimization algorithm used Al-Biruni to explore and exploit, mimicking swarm intelligence. Wild swarms live together and share information. They take turns searching for food and avoiding predators. Members worked together in smaller groups and on their own. In swarms, ant and bee colonies cooperate. Each member's actions strengthen the swarm. The colony's workers feed the warriors. The fact that people are generally divided into smaller groups to focus on specific activities at specific times inspired BER. Exploration and exploitation are used to find the best optimization solution. Here, BER divides people into two groups, one for each pursuit. BER's exploitation and exploration stages exhaustively search the search space to prevent local optimums. Most cooperative optimization approaches need collaborative action after each iteration, which may maintain inferior solutions or converge to a local optimum. To avoid this, BER maintains a pool of agents that collectively search for a bigger search space over time. If BER's performance doesn't improve after three solution mutation iterations, search space exploration will expand significantly.BER, an optimization algorithm, utilizes a vector representation to denote individuals in the population. The vector is represented as 40. The exploration group comprises 70% of the population, while the exploitation group consists of 30% of the population. To improve the fitness values of individuals in each group, the number of individuals in the exploitation group is increased from 30 to 70% over the optimization iterations. Conversely, the number of individuals in the exploration group decreased from 70 to 30% over the iterations. This process enhances the global average of the fitness of individuals, promoting more significant improvements. To ensure convergence of the optimization process for the population, the elitism strategy is utilized, whereby the leading solution is kept if no better solution is found. If a solution's fitness does not improve significantly over three iterations, the algorithm applies a mutation operation to generate another exploration individual as it may be a local optimum.The proposed algorithm divides the population into subgroups to balance the tasks of exploitation and exploration. The number of individuals in each group is dynamically adjusted to optimize the fitness values. Initially, the population is divided into two groups: an exploration group and an exploitation groupExploration involves both identifying promising regions in the search space and avoiding getting stuck in local optima by moving towards the best solution. To achieve this, individuals in the exploration group use a strategy of searching for potential areas around their current position in the search space. They do this by repeatedly searching for better options in terms of fitness value among the surrounding feasible alternatives. The BER optimization algorithm employs the following equations to guide this process.The mission of the exploitation group is to enhance already implemented strategies. In each cycle, the BER determines each person's fitness level and picks out the top performer. As can be seen in the next sections, the BER uses two distinct methods to achieve exploitation.The following equations are used to move the search agent toward the best solution:The area around the most promising leader is the most promising one. Therefore, some people look around locally for the greatest answer, hoping to unearth something better. The following equation is used by the BER to do this operation:The BER conducts research using a variety of methods, one of which is mutation. It is a genetic operator that is used to develop and maintain variation in population populations. It is possible to think of it as a probabilistic local random disruption of one or more components in people. It stops early convergence by assisting in the avoidance of local optima, and such a movement in the search field acts as a springboard to another interesting subject. The mutation is significant for the outstanding exploration possibilities offered by the BER.The best solution is selected for the following iteration by the BER, guaranteeing the high quality of the solutions found. While the elitism approach improves algorithm performance, it may lead to unexpected behavior when used for multimodal functions. It's worth noting that the BER's mutation procedure and seeking around explorers in the group result in excellent exploring abilities. The BER's robust exploratory capabilities allow it to delay convergence.During this part of the study, a working model of the SMG system is developed and tested in the laboratory. This prototype is made up of photovoltaic panels, a battery, a programmable logic controller (PLC), an analog input module, a smart meter, an inverter, AC loads, motors, lighting, relays, and current sensors. Figure The value of the system loads that are measured from the sensors are included as one of the system inputs. Other examples of system inputs include the solar panel output value, the battery state of charge %, the inverter output for loads, and the value of the solar panel output. On the other hand, the signals sent to the relays, which are employed in the process of controlling the loads, are regarded as system outputs. Figure i.The disconnection and reconnection of loads are governed by one of three distinct kinds of procedures. The first difference is that in the event of a shortcoming in the inverter settings, the sheddable loads will be disconnected until the issue has been resolved. The second strategy involves disengaging certain types of loads once a deficit is detected and then reconnecting them after a certain amount of time has elapsed, regardless of whether or not the deficit has reached the maximum allowable level. This category of load is referred to as shiftable load. Third, when there is a shortage, the quantity of electricity that is given to the load can be regulated until the issue is resolved. This can be done by managing the amount of electricity that is sent to the motors or the DC lamps; this category of load is referred to as a controllable load.ii.The priority of loads at various points during the day is used to determine how the order of these operations is determined and regulated. When a shortfall is discovered at the start of the day, the shiftable load, the sheddable load, and the controlled load are separated. When a power shortage hits in the middle of the day, however, the procedures for disconnecting the lines are carried out in a different order. As an illustration, first controllable, then sheddable, and last shiftable. The priority level of the same load at various times throughout the day results in a variable level of urgency. For instance, the lamps that are active during the afternoon period do not have a priority, which means that they can be moved during the first action. On the other hand, the identical lamps that are active during the night period have a high priority and therefore cannot be moved during the first action.iii.The price is set to be higher during peak hours and lower during off-peak hours.iv.Depreciated costs are calculated by multiplying the sensor readings by the current market price.The PLC controller is linked to the rest of the system via an OPC-UA Server/Client, and the PLC and the Excel sheet serve as storage for the communication protocol known as dynamic data exchange protocol. The system's output consists of relay signals used to manage the loads in the system. Here's how the controller gets put to use.For the purpose of designing the control system, TIA portal software is utilized. WinCC software was used to create SCADA systems, and these systems were then linked to the PLC control system. The processes of disconnecting and reconnecting loads can either be handled automatically, as was explained earlier, or manually, by clicking buttons on the screen, as illustrated in Fig. The prices of the electricity that was taken were also retrieved, as shown in Fig. Through the combination of IOT and Telegram, the Telegram Bot may be able to perform the same tasks as SCADA on a mobile phone. The Telegram Bot and the control system, which was programmed on the red node platform, were able to connect with one another thanks to the OPC-UA Server/Client communication protocol. The Bot is able to issue commands to the system as well as query about the settings that are currently in place. A real-world application of the Telegram Bot commands is demonstrated in Fig. If you are in manual mode, you can start the DR actions by typing 'Start shedable action' or 'Start shiftable action' or 'Start controllable action'.Also, you can stop these actions by replacing the 'Start' word with the 'Stop' word.Concerning the controllable load, if you are in manual or automatic mode, you can change its percentage by typing 'Set the controllable load to 30%' or 50%, or 70%.Change the operating mode by typing 'Set mode to manual' or 'Set mode to automatic'.Knowing the current Battery System Capacity Percentage value by typing 'What is the battery capacity percentage?'.Knowing the current PV System Input Power value by typing 'What is the PV input power?'.Knowing the current Plant Output Active Power value by typing 'What is the plant output active power?'.Knowing the current Theft Power value by typing 'What is the theft value?'.Knowing the current Theft Cost value by typing 'What is the theft cost value?'.Knowing the current Total Load Cost value by typing 'What is the total load cost value?'.You will not only take action, but you will also get the current readings of the system like:This paper introduced a novel approach to energy management by combining demand response with the ToU pricing model. With the help of the Al-Biruni Earth Radius optimization method, it was conceivable to strike a balance between minimizing energy consumption and maximizing human comfort. To further fortify the smart microgrid's safety, a theft detection device that tracks the gap between electricity withdrawal and consumption has been implemented. The proposed system also included the management of inverter and smart meter-connected loads, allowing for flexible responses to power outages. Daily demand trends were used to determine the order of priority for load shedding, and management systems were developed to shed, shift, or control loads as needed. SCADA solution, which included the WinCC software and an IoT Telegram Bot, also allowed for effective monitoring and control, even from far away. Many promising avenues for further study and advancement have been identified as a result of this paper. Advanced machine learning algorithms could be used to improve the theft detection mechanism; further optimist the BER algorithm; investigate strategies for dynamic load management; incorporate energy storage solutions; bolster cybersecurity measures; encourage user participation; evaluate environmental impact; explore grid expansion and interconnection; and so on. A more sustainable and resilient energy future can be achieved by addressing these issues, which will contribute to the ongoing development of smart microgrid energy management.


It is still under debate whether the occurrence of this benefit is because of the semantic association between the preview and target words or because of the contextual fit of the preview word in the sentence context.Numerous studies have confirmed that skilled readers can benefit from a semantically related preview word  and semantic relatedness , were manipulated, and we further strictly controlled for syntactic plausibility in the present study.The results showed that the first-pass reading times of the target words were significantly shorter in the plausible preview condition than in the implausible preview condition. However, the main effect of semantic relatedness was found only in the gaze duration measure.The pattern of results revealed that semantic plausibility affects the semantic preview benefit preferentially in Chinese reading, supporting the contextual fit account. Our findings have implications for a better understanding of parafoveal processing and provide empirical support for the eye-movement control model. A large number of studies have confirmed that skilled readers can preview the upcoming word that appears to the right of the fixation point , which facilitates the processing of the word when it subsequently becomes fixated on , 2009. TEye-tracking technology and the gaze-contingent boundary paradigm  were useA much-debated question is why SPB is observed in reading. There are two explanations for the SPB reported in reading studies. One explanation is that SPB may arise from the semantic associations between the preview and target words. The processing of the semantically related word would directly activate the target word, which is similar to the underlying mechanism of semantic priming effect . Early rAnother alternative theory, however, argues that the SPB may reflect the extent to which the preview word contextually fits with the sentence, rather than the semantic association between the preview and target words . On thisCompared to alphabetic writing systems, Chinese as a logographic writing system has distinct characteristics  that proe.g., the preview should be a noun, but it was a verb). For example, in the sentence “Toby kept his money in a large barn/woke because he lived on a farm”, the implausible and unrelated preview “woke” was not only semantically implausible but also constituted a syntactical violation. Therefore, the SPB effect found in previous studies may also reflect readers’ sensitivity to syntactic plausibility. That is, the SPB reported in previous research might result from syntactic plausibility in semantically related or contextually plausible preview conditions, but not arise entirely from semantic spreading between the preview and target words nor the fitted context of plausible previews.In addition, previous research in the field paid little attention to syntactic plausibility, which may have contributed to the inconsistent findings and the debate over the SPB effect between the two accounts mentioned above. Specifically, previous studies on the SPB did not rule out the effect of syntactic plausibility on the preview benefit. While related/plausible previews are always syntactically plausible, unrelated/implausible previews may not be . The difhe eventually found a spare stool/uncle/begin behind the crowded bar.). Moreover, the first-pass reading times of the target words could be significantly shortened when their previews were syntactic and semantically plausible compared to when their previews were syntactic violations but semantically plausible (her plane will probably refuel/depart/landed later than expected this afternoon). The pattern of results showed that readers can extract syntactic information from the parafovea at a relatively early stage and benefit from syntactic acceptability in parafoveal processing.Recently, the effects of syntactic information on parafoveal lexical processing in reading have been demonstrated in eye-movement studies . Veldre In Chinese reading, several studies have shown that syntactic cues play a minor role in reading than that semantics and context information . HoweverThe present study aimed to investigate the underlying mechanisms of the SPB by using eye-tracking technology and the boundary paradigm. Two independent factors, preview plausibility (plausible/implausible) and semantic relatedness  were manipulated, and syntactic plausibility was strictly controlled in the present study. Our study does not aim to systematically investigate the effect of parafoveal syntactic information on SPB, but rather aims to further explore the results pattern of SPB by excluding the interference of syntactically implausible information based on previous studies . TherefoThis study was approved by the Research Ethics Committee of the Faculty of Psychology, Tianjin Normal University , and conducted in accordance with the principles of the Declaration of Helsinki. All participants volunteered to participate in the experiment and signed informed consent.f = 0.25 and the significance level reached α = 0.05, a total of 54 subjects were needed to achieve 95% statistical test power. Compared with the 20 sentences under each condition in previous studies  from Tianjin Normal University to participate in the eye-tracking experiment. All participants were native Chinese speakers with normal or corrected-to-normal vision. Participants were paid 30 RMB for their participation in the experiment.G * power 3.1 software was used to calculate the number of subjects in the two-factor within-subject experimental design. When the effect size reached  studies , there aF = 0.12, p = 0.95) or visual complexity  under the five preview conditions. Word frequency was based on the SUBTLEX-CH database (Note that most Chinese words (72%) are two-character words ; therefodatabase .Each target word was embedded into a sentence with a length of 17–25 words  the preview word. The rating results of another 20 participants showed that 98% of the nouns were confirmed to be nouns, 97% of the verbs were confirmed to be verbs, and 98% of the adjectives were confirmed to be adjectives. The two ratings ensured that our syntactic manipulation of the preview words was valid.Twenty participants were recruited for the cloze task used to measure the predictability of the five preview conditions, in which the participants were asked to write the word that is most likely to come next according to the given sentence frame up to the pretarget word. The results showed that the predictability of all five conditions was very low (<2% on average).ts > 49.00, ps < 0.001). Moreover, the differences in the semantic relatedness of the two related previews or the two unrelated previews did not reach significance .Another group of 20 participants rated the semantic relatedness of the preview and target words on a seven-point scale, and the results showed that the plausibly related and implausibly related previews had significantly higher semantic relatedness than the plausibly unrelated and implausibly unrelated previews ; the differences between the two plausible previews and the identical previews were not significant ; and there was no significant difference between the two implausible previews .Another 20 participants rated the contextual plausibility of the sentence frames up to each preview word on a seven-point scale. The results showed that the identical, plausible previews were all significantly more acceptable than the implausible previews . After successful calibration, each trial started with a fixation point appearing in the screen center, and then, a fixation box appeared on the first character at the beginning of the left side of the sentence. Once the participants stably fixated on the fixation box, the sentence was presented on the screen. The three-point calibration was repeated after every fourth trial and during the experiment when necessary. Each participant was randomly assigned to one of the five blocks. In addition to 80 experimental sentences, there were 60 filler sentences included in each block, and 25% of the sentences were followed by a simple “yes” or “no” judgment to ensure that the participants completed the experiment carefully. The first six sentences were practice sentences, and the remaining 140 sentences were presented randomly. The experiment lasted approximately 40 min.The comprehension accuracy rate of all participants was higher than 90%, indicating that the participants carefully read the sentences. Fixations that were shorter than 60 ms or longer than 600 ms were not included in the analysis . Trials first fixation duration (FFD), the duration of the first fixation on the target word during the first-pass reading; single fixation duration (SFD), the duration of the fixation when there is only one fixation on the target word during first-pass reading; gaze duration (GD), the total time of all first-pass fixations on the target word; and skipping rate (SR), the probability of the target word being skipped during the first-pass reading.The following four commonly used eye-movement measures for investigating the parafoveal preview benefit were analyzed : first flme4 package  the plausibility preview effect, the difference between plausible and implausible previews; and (c) the semantic relatedness effect, the difference between semantically related and unrelated previews. The log-transformed analysis and untransformed analysis of fixation duration measures obtained the same significance pattern. Therefore, we reported the untransformed model results for transparency. For all the analysis results, a t/z value of greater than 1.96 indicated a significant difference (p < 0.05).We analyzed the target word region and tested the following planned contrasts: (a) The descriptive statistics of each measure of the target word are shown in ts > 5.67, ps < 0.001) and a marginally significant preview effect in the skipping rate measures .There was a significant preview effect in all fixation duration measures  and in the skipping rate measures . The fixation durations of the target words were shorter in the plausible preview condition than in the implausible preview condition, and the readers were more likely to skip the target words when the preview words were plausible in the first-pass reading. However, the semantic relatedness effect was found only in the gaze durations , and no significant differences were observed between the semantically related and unrelated preview conditions in the other fixation duration measures  or the skipping rate measure . In addition, the interaction effect of preview plausibility and semantic relatedness did not approach significance across any of the fixation duration measures  or in the skipping rate measure .A different pattern occurred in the plausibility preview effect and the semantic relatedness preview effect. A significant plausibility preview effect was observed in all fixation duration measures . To further test the reliability of this result, the LmBF  function in the BayesFactor package  of the full model (including the main effect and the interaction effect between the two variables) to the model containing only two main effects. If the BF is less than 1, the null hypothesis is supported, indicating that there is no interaction effect. If the BF is greater than 1, the alternative hypothesis is supported, indicating that there is an interaction effect. If the BF is greater than 3, it provides substantial evidence to support the full model and indicates that there is an interaction effect .Since we focus on the interaction effect between preview plausibility and semantic relatedness, we calculate the ratio of the Bayesian factor  than for the relatedness variable (t = 2.54) in the gaze duration, as shown in We also observed the semantic relatedness effect in gaze duration, suggesting that semantic relatedness may also play a role in the occurrence of SPB. This result differs from those of previous studies . By orthNo reliable interactive effects were observed between the two independent variables in the present study, suggesting that the occurrence of the SPB seems to be independently affected by the preview plausibility and semantic relatedness. This pattern of results may be due to preview plausibility and semantic relatedness playing different roles in sentence processing. The former involves the integrative comprehension of preactivated semantic information and the preceding sentence context , while tCombining the finding obtained in the present study detailing that preview plausibility has an earlier and stronger effect on SPB than semantic relatedness, we prefer to use the contextual fit account to explain the SPB effect. Unlike alphabetic scripts, there are no obvious physical markers (such as intercharacter/interword spaces) between characters/words for word segmentation in written Chinese text, so readers need to rely heavily on the sentence context to perform word segmentation . This hiOn the other hand, the effect of semantic relatedness on the SPB mainly relies on the semantic spreading activation between the preview and the target words rather than on the sentence contextual information or knowledge experience. Our research shows that the effect of semantic spreading lags behind that of contextual information in the process of word identification for sentence comprehension. Specifically, semantic spreading activation begins after the reader processes the semantic information of the preview word . In the I) introduced by E–Z Reader 10 (These findings might have some implications for developing reading models. Based on previous research , we disceader 10  provideseader 10 . A delayIn conclusion, this study aimed to gain a better understanding of the SPB. Our study found that preview plausibility plays an earlier and more comprehensive role in the occurrence of the SPB, while semantic relatedness has a much later and limited effect on it. The results reveal that the plausibility relationship between the preview words and their contextual sentence may preferentially affect the SPB, supporting the context fit account.


ASR-vs.-distortion curves with different attack scenarios under CIFAR10 preliminarily demonstrates the effectiveness of the proposed method that even the highest-capacity attacker cannot easily outperform the attack success rate associated with the ensemble smoothed model, especially for untargeted attacks.Deep neural networks (DNNs) have been shown to be susceptible to critical vulnerabilities when attacked by adversarial samples. This has prompted the development of attack and defense strategies similar to those used in cyberspace security. The dependence of such strategies on attack and defense mechanisms makes the associated algorithms on both sides appear as closely processes, with the defense method being particularly passive in these processes. Inspired by the dynamic defense approach proposed in cyberspace to address endless arm races, this article defines ensemble quantity, network structure, and smoothing parameters as variable ensemble attributes and proposes a stochastic ensemble strategy based on heterogeneous and redundant sub-models. The proposed method introduces the diversity and randomness characteristic of deep neural networks to alter the fixed correspondence gradient between input and output. The unpredictability and diversity of the gradients make it more difficult for attackers to directly implement white-box attacks, helping to address the extreme transferability and vulnerability of ensemble models under white-box attacks. Experimental comparison of  Deep learning techniques have been successfully applied in various computer vision applications, ranging from object detection  based on Monte Carlo simulations.For the robustness evaluation of the proposed method, this article considers the attack and defense game idea as a starting point, assuming that the attacker knows the defense strategy, and simulates a series of possible adversarial game processes for a more comprehensive evaluation. The different capabilities of the attack scenario are set up and the potential defense risks are assessed using attack success rate versus distortion  to the input images and minimizing the corresponding classification losses  are randomly adjusted to dynamically structure the ensemble model at each time inference prediction request making the output of candidate models in SEM have an additional mathematical expectation in terms of probability of occurrence. However, the probability of occurrence of a particular candidate model under the SEM is assumed to be determined by the expectation E(fk)occurrence = ωk and statistically independent of the prediction expectation. Therefore, as shown in Equation (4), the stochastic ensemble and SWEEN models can be equivalent in terms of output expectations. The theoretical improvement of the robustness radius by the SWEEN model  of the predictive performance of each sub-model. The models marked in red did not meet performance requirements and were excluded from subsequent experiments. Although some simple models, such as AlexNet and shallow VGG, were unable to achieve stable smoothed prediction, unsmoothed models were used for the SEM. The experimental results in Section 4.5 further demonstrate that the heterogeneity of the model collection plays a crucial role in the robustness of the stochastic ensemble.The heterogeneous redundant model collection is obtained by separately training a smoothed model on the CIFAR10 dataset  as follows:The attack success probability is redefined as the proportion of Monte Carlo simulations in which each ASR-vs.-distortion curves. In this case, the threshold α is set at 0.3 to evaluate the random smoothing model.The abstention threshold α is a parameter used to limit the probability of returning an incorrect prediction in order to control potential empirical model risk  with attackers ASR-vs.-distortion curves for untargeted transfer-based attacks. A, B, C, and D represent different attack scenarios, while the contrast methods F, G, H, and I are shown as dashed curves. Compared to the baseline models, we can observe that the ensemble model is highly vulnerable to white-box attacks, even worse than the single models. The random smoothing method improves the robustness of a single model, and the ensemble-smoothed model further improves the robustness and addresses the vulnerability of the ensemble under white-box attacks. Among all attack methods, attacker B has the worst attack performance, indicating that protecting the model from frequent access to gradient information at each iteration is crucial for SEM robustness. Attacker D, who has partial knowledge of the model collection but ensemble attributes in each iteration, can achieve transfer attacks through the ensemble and achieves similar robustness performance (even better than PGD) compared to attacker A. However, comparing the performance of attackers C and D, the SEM does not improve the attack transferability effect as a regularization method. This reveals the importance of protecting the model collection for SEM robustness. When the attacker has a higher transferability attack algorithm (for the MIM and PGD), the benefits of transferability are only for attacker D and are no longer attained by SEM. For the ensemble smoothed model (F curves) that has the SOTA performance between the contrasting baseline methods, the best attack performance cannot easily exceed the attack success rate associated with it.ASR-vs.-distortion curves for targeted transfer-based white-box attacks. When comparing different attack algorithms, the improved transferability of the PGD method does not significantly improve the attack performance under SEM. However, its robustness is significantly improved against the momentum-based attack, indicating that the randomness of the gradient at the model level has some impact on the confusion of the gradient direction. The variation in the attack knowledge of model collection between A and C does not significantly affect the robustness of SEM when against targeted attacks. However, contrary to the conclusion drawn from untargeted attacks, the robustness performance of SEM under A and C does not consistently exceed that of the ensemble smoothed or single smoothed model, demonstrating the lack of heterogeneity of the model in the gradient direction. However, as the detailed results in the second line of A, B, C, and D, the weakest attack performance is exhibited by B . Combined with the results of the untargeted attacks, we suggest that reducing the frequency of ensemble changes is critical for SEM when the model collection and ensemble attributes can be obtained by an attacker.The results of an untargeted source-based black-box attack are depicted in In comparison, the results for the targeted source-based black-box attacks that show a decrease in overall accuracy are shown in via stability training . In addition, we compare a stochastic ensemble consisting of a single-architecture CNN with different smoothing parameters. To ensure comparable prediction accuracies with our method, we choose the WRN  to further improve the robustness against white-box attacks, adaptive control of the ensemble changes based on attack detection is a crucial research direction; (2) under the query-based black-box analysis, the smooth parameter selection probability of the ensemble strategy is a crucial optimization direction for this study; (3) for practical applications, both the number of parameters of the model and the forward efficiency of the ensemble prediction should be considered. In this study, the robustness is evaluated on the CIFAR10 dataset, but there are practical application problems because of the large training cost. Therefore, the light weight of the ensemble model is an important research direction.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.RQ: conceptualization, validation, software, and writing—original draft. LW: methodology, resources, and supervision. XD: funding acquisition, investigation, and supervision. PX: supervision. XC: project administration and funding acquisition. BY: writing—reviewing and editing. All authors contributed to the article and approved the submitted version.


The promising outcomes were solid proof of the effectiveness of MISIRoot as a potential solution for identifying WCR infestations before the plant shoot showed significant symptoms.Western corn rootworm (WCR) is one of the most devastating corn rootworm species in North America because of its ability to cause severe production loss and grain quality damage. To control the loss, it is important to identify the infection of WCR at an early stage. Because the root system is the earliest feeding source of the WCR at the larvae stage, assessing the direct damage in the root system is crucial to achieving early detection. Most of the current methods still necessitate uprooting the entire plant, which could cause permanent destruction and a loss of the original root’s structural information. To measure the root damages caused by WCR non-destructively, this study utilized MISIRoot, a minimally invasive and in situ automatic plant root phenotyping robot to collect not only high-resolution images but also 3D positions of the roots without uprooting. To identify roots in the images and to study how the damages were distributed in different types of roots, a deep convolution neural network model was trained to differentiate the relatively thick and thin roots. In addition, a color camera was used to capture the above-ground morphological features, such as the leaf color, plant height, and side-view leaf area. To check if the plant shoot had any visible symptoms in the inoculated group compared to the control group, several vegetation indices were calculated based on the RGB color. Additionally, the shoot morphological features were fed into a PLS-DA model to differentiate the two groups. Results showed that none of the above-ground features or models output a statistically significant difference between the two groups at the 95% confidence level. On the contrary, many of the root structural features measured using MISIRoot could successfully differentiate the two groups with the smallest  Zea mays L.) is an essential crop in the world due to its economic significance, versatility, and its critical role in sustainable agriculture. In the U.S. in 2022, the total yield was about 13.7 billion bushels, and the harvest area was 79.2 million acres [Diabrotica barberi Smith & Lawrence) (NCR) and the western corn rootworm (Diabrotica virgifera virgifera LeConte) (WCR) are major economic pests of corn which cost U.S. producers about $1 billion annually in yield losses and input costs to control them [Maize (One of the common methods for assessing corn rootworm infestation in the root area involves cutting a 7-inch (18-cm) cube of soil around the base of the plant and lifting it out of the ground. The attached soil needs to be carefully removed from the roots and then the larvae can be examined either on a contrasting dark surface or by saturating the roots in salt water. This causes the larvae to float to the surface, making them easy to extract and count. To assess the damage caused by corn rootworm larvae in a certain field, the procedure needs to be repeated multiple times . A groupIn recent years, several innovative methods have been developed to address some of the challenges in plant root phenotyping. For instance, a commonly applied approach called “minirhizotron” involves inserting a camera into a transparent tube pre-buried underneath the plants ,7,8. HowThis study utilized MISIRoot, an advanced and minimally invasive root phenotyping technology that enables in situ analysis of corn root structures in common soil environments . This syA total of 32 corn plants were prepared in the Lily greenhouse at Purdue University, West Lafayette, USA. Half of the plants were randomly assigned to the inoculated group, while the remaining plants were assigned to the control group. A total of 10,000 WCR eggs were ordered and incubated in a closed growth chamber at 27 degrees Celsius and 70% relative humidity to facilitate hatching. Most of the WCR eggs started to hatch when the corn plants reached the V2 growth stage. A total of 100 WCR larvae were delivered to the crown root section of every plant in the inoculated group using a miniature paint brush as shown in After the inoculation process, the roots that were inoculated by the larvae were gently covered with soil. Nylon nets were also placed over the pots to prevent the larvae from escaping from the pot. The WCR larvae will undergo metamorphosis and develop into beetles in approximately 3 to 4 weeks. Since adult WCR pests have strong motility that may result in uncontrollable contamination of the environment, the sampling process was initiated 15 days after the inoculation, before the pests turned into adults as shown in Before the MISIRoot system, side-view images of the corn plants were captured using a Nikon D5300 camera, with the aperture set to f/9, exposure time set to 1/60 s, and ISO set to 200. During the data collection process, a sampling area with a diameter and depth of 100 mm was designated, resulting in the generation of 130 sampling tunnels around each plant. Each tunnel contained 35 viewpoints, leading to the acquisition of 4550 images per plant. Once completed, all plant materials, including the soil and pots, were decontaminated using an autoclave. After sterilization, the roots were carefully extracted and washed for visual examination and side-view imaging.This project aimed at demonstrating that MISIRoot was capable of detecting the WCR damage to corn plant roots before symptoms of lodging or nutrient deficiency become visible in the shoot section. To achieve this, two different approaches were used to analyze the side-view RGB images of the corn plants. In the first approach, the color information and the morphological features of the plant leaves were extracted as the input of a Partial Least Squares-Discriminant Analysis (PLS-DA) modeling process, which would be able to tell if the two treatment groups could be well separated. The PLS-DA model was specifically selected because it has been extensively utilized in plant phenotyping applications to identify plant stress thanks to its exceptional ability to manage high-dimensional data and mitigate the issues of multicollinearity ,20. The Before applying both approaches, the images were calibrated for ambient light variations using the white reference board positioned behind the corn plant. The corn plants were calibrated and segmented from the image to remove any ambient light and background noises. After segmentation, the average values for red (R), green (G), and blue (B) were extracted from the remaining pixels. To facilitate analysis, the RGB color space was then converted to the HSV  color space, which separates the brightness component  from the chromatic components (hue and saturation). The HSV color space is commonly used in machine vision and image processing applications, including plant phenotyping , as it et-tests.In the first approach, partial least squares discriminant analysis (PLS-DA) was conducted by integrating the RGB and HSV color information with leaf area and plant height. Monte Carlo cross-validation (MCCV) was used to randomly partition the data into training and testing sets in a 70:30 ratio for 1000 iterations. For the second approach, 10 vegetation indices based on RGB color space were utilized as shown in The data obtained from the MISIRoot system can be divided into two parts: raw images and corresponding 3D coordinates of each image.A total of 135 images in which the root is visible were selected for training a deep convolutional neural network model for high-throughput image processing. In each image, every visible root section was manually labeled with square-shape bounding boxes indicating both the existence and the types of roots found in the image. Types of the root were grouped into “thin” and “thick” categories based on the thickness and color of the root detected in the image. When the root was wider than 60 pixels in the image and had a slight yellow or brown color, it would be labeled as a “thick” root, otherwise, it would be labeled as a “thin” root. A total of 95 labeled raw images (70%) were randomly chosen for training, and the remaining 40 images (30%) for validation. Before being fed to the model, the data was augmented with random horizontal or vertical flips based on a 50% random chance. The base model structure selected for training was Faster R-CNN with a ResNet-50-FPN backbone ,33. The After processing with the deep learning model, the data can be further categorized into four main parts: number of detected roots for each plant, color information of the detected roots, classification of root thickness, and coordinates for each identified root. For effective utilization of the data, it is crucial to obtain accurate color information on the roots. A clean segmentation that eliminates soil background, soil particles on the roots, and any specular reflectance present on the roots is a crucial step in extracting the color information, as illustrated in t-tests were then conducted to ascertain the presence of any statistically significant differences between the two groups for each of these values. Further analysis was conducted on the classification of root thickness, as well as the coordinates of the detected roots The coordinates can be used to determine the depth, which is the distance below the surface, and radius, which is the distance from the plant’s main stem, for each identified root. The purpose of this analysis was to gain a better understanding of the distribution of roots and the extent of damage inflicted upon them. The working flow for analyzing the data collected by the MISIRoot system is illustrated in The average R, G, and B values, as well as the H, S, and V values of the remaining pixels from the segmented images were calculated and separated into two groups based on the thickness classification of the roots. To determine whether there were significant differences between the above-ground portions of two groups of corn plants, the first approach involved constructing a PLS-DA model using the information extracted from the side-view images. Following cross-validation, the average training accuracy and average testing accuracy were found to be 48.69 percent and 50.27 percent, respectively. While the testing accuracy is higher than random guessing, it is still considered relatively low and indicates that the model has limited discriminative power.p-values in The second approach employed 10 vegetation indices to identify areas where the two groups exhibit discernible differences in nutrient stress. The p-values for the R, G, B, and V values for thin roots were found to be smaller than 0.05, indicating that there were statistically significant differences between the inoculated and control groups for these values.The outcomes obtained from high-resolution RGB images collected by the MISIRoot are presented in The results presented in t-tests being performed. p-values for the number of thin roots detected, and the average depth of thin roots exhibit significant differences between the two groups, as they are substantially lower than 0.05. The thin roots showed significant differences between the control and inoculated groups, whereas no significant differences were observed for the thick roots.Further analysis was conducted on the number of roots detected, as well as the average depth and radius of the roots. The roots were categorized into two groups based on their thickness, resulting in a total of six To gain a deeper understanding of the results, the number of detected thin roots was analyzed with their depth and radius. The sampling depth for this project was 100 mm and the sampling radius for this project was 50 mm. The depth was divided into five regions, with each region covering a depth of 20 mm. The radius was divided into five concentric circles, with each circle covering approximately 10 mm, as shown in p-values from all tests on a −log10 scale and the orange dashed line represents a p-value of 0.05. While none of the p-values for vegetation indices were smaller than 0.05, certain p-values for data collected by the MISIRoot system were found to be significant at this threshold. While color information for thin roots exhibits the capability to distinguish between the two groups, the average depth of thin roots and the number of thin roots detected yield the most promising results, as evidenced by the significant p-values of 6.0481 × 10−5 and 1.5791 × 10−6, respectively.The results were compared to provide a more direct way of interpreting the differences between data collected by the DLSR camera from the shoot section and data collected by the MISIRoot. Most of the current WCR detection methods necessitate the complete destruction of plants and rely on the emergence of severe symptoms, such as “gooseneck lodging” in the shoot section . HoweverThis paper focuses on detecting damage caused by WCRs at early growth stages using an innovative root phenotyping system called MISIRoot. The larvae were specifically placed on the crown root section of the corn plants, resulting in more pronounced damage occurring in the shallower region. Moreover, root damage in the shallower region inhibits the development of finer roots. Because MISIRoot is capable of estimating the root density distribution, the thin root damaged by WCR, which primarily infests root hairs and outer root tissue , can be t-tests. It is also worth noting that certain factors, such as the number of larvae and the time of inoculation, were carefully controlled for experimental purposes. Challenges may arise in future applications when trying to determine the ideal detection time window. Numerous other factors, including environmental and soil conditions, must be considered to determine the appropriate timing for implementing the MISIRoot system in similar tasks.Moreover, the unique design and workflow of the MISIRoot system make it suitable for field applications. It can be conveniently transported by ground-based vehicles or installed on center-pivot irrigation systems. While the current design of the system can benefit from improved throughput, the challenges can be addressed by incorporating additional cameras. The deep learning model achieved a high accuracy with the current dataset and experiment setup. Some limitations may exist when dealing with different soil colors or different plant root stress. The model performance could also be affected by some other factors, such as the images being blurred or blocked by the soil. The model can be further trained with newly labeled data coming from the new environment. Nonetheless, it is important to acknowledge that the sample size was limited due to resource constraints. Conducting additional experiments or adding more replicates can enhance statistical power in Through the analysis of side-view images of corn plants and data collected by the MISIRoot system, this study demonstrated the system’s ability to detect damages caused by WCR larvae to the corn root system before visible differences appeared in the above-ground portion of the plants. The data collected with MISIRoot was first processed using a CNN model, which labels the detected root segments in the images. The root color exhibited noticeable differences between the inoculated and control groups, which was confirmed by visual examination after the roots were extracted. Better separation between the treatment groups was achieved by integrating additional features, such as the number of roots detected, the average depth and radius of the detected roots, and the thickness of the roots. Overall, the MISIRoot system has demonstrated its potential to revolutionize the way to assess WCR infestation in an affordable and non-destructive manner.Future research can involve time series analysis to further advance our understanding of the damages caused by WCR larvae and refine the detection protocol. It is possible to capture the damage development by repeating the measurements over an extended period of time. Additionally, such analysis can facilitate a comparison between the MISIRoot system and other established methods, helping determine whether the MISIRoot system is capable of detecting damages earlier than existing approaches. Additionally, the throughput of the system can be further improved by adding more imaging heads and the quality of the images can also be further enhanced by equipping more advanced cameras.


The transport truck is one of the important equipment for open-pit mines, and predicting the truck's fault time is of great significance in improving the economic benefits of open-pit mines. In this paper, we discuss the reason for the large prediction error of the exponential smoothing method. Then, we propose a novel nonlinear exponential smoothing method (ESNN) for predicting the truck's fault time, and demonstrate the equivalence between our approach and the neural network structure. Finally, based on the augmented Lagrange function, the solving method of ESNN is proposed. We conduct experiments on real-world datasets and our results demonstrate the effectiveness of ESNN in comparison to existing state-of-the-art methods. Our approach makes it easier for maintenance personnel to predict fault situations in advance and provides a basis for enterprises to develop preventive maintenance plans. The transport truck is one of the most important equipment for open-pit mines. In the actual production process, the transport truck has a high fault rate and frequent maintenance due to the poor work environment and improper operation. Meanwhile, maintenance costs are very high3. The reasonable use and scientific maintenance of trucks can improve the reliability of the open-pit mines process. Therefore, exploring the principle of truck malfunctions and developing scientific maintenance plans is of great significance.Most open-pit mines in China adopt intermittent systems of electric shovel trucks and semi-continuous mining systems9 can reduce mining costs to a certain extent by quickly identifying fault types and accelerating equipment maintenance.The existing fault maintenance mode of open-pit mine trucks is mainly concentrated in the later stage. Due to the lack of maintenance plans and equipment preparations, the company is unable to provide timely maintenance information, which will cause equipment downtime and affect production. To address this challenge, fault diagnosis technology10 propose a novel diagnosis method for truck gearboxes based on BP neural networks, and provide criteria for identifying safety hazards in gearboxes. Hu et al.11 build a classification model based on truck data and propose a method for predicting truck conditions and performance, which significantly improves the efficiency of trucks. Bai Runcai et al.12 propose a method for predicting the fault rate of trucks based on Mallat algorithm and ARMA algorithm. Zhang Yongqiang et al.13 propose a novel diagnosis method for motor bearing, which uses RBF neural networks and fuzzy integrals for fault diagnosis.Yang Huan et al.19 is gradually becoming an effective method for reducing the risk of sudden faults by predicting its future state based on degradation characteristics. In recent years, the rapid development of artificial intelligence technology has provided new ideas for fault prediction. Zhang et al.20 build a novel index of performance degradation based on the combination of principal component analysis and support vector data. Li Wei et al.21 use the Long Short Term Memory (LSTM) network to predict the future status and the K-Nearest Neighbor (KNN) algorithm to predict fault according to the relationship between the running status and faults. Li Zhijun et al.22 propose a novel prediction method of fault based on Dynamic Inner Principal Component Analysis (DIPCA), which predicts three different types of faults. For the problems of fault time, Liu Wei et al.23 propose a prediction method with the Metropolis–Hastings (MCMC) algorithm. Zhang Qingliang et al.24 use the isometric mapping algorithm to perform feature reduction, and use LSTM to predict the remaining life of automobile gears.As a post-processing method, fault diagnosis fails to reduce the fault rate of equipment. However, fault prediction28 and the reasons for the large prediction error of the truck's fault time in open-pit mines. On this basis, we propose a nonlinear exponential smoothing method and construct an equivalent neural network model to estimate its parameters. Our approach can significantly improve the prediction accuracy of the exponential smoothing method. Table The related work mainly revolves around two perspectives: predicting fault types and predicting equipment's remaining life. However, little research has focused on predicting the truck's fault time in open-pit mines. In this paper, we analyze the principle of the exponential smoothing algorithmWe prove the equivalence between the exponential smoothing model and the neural network;We propose the neural network model based on the nonlinear exponential smoothing method.The organizational structure of this article is as follows: “Exponential Smoothing (ES) was formally proposed by Brown in 1959, who believed that the time status is stable so that the time series can be reasonably extended. Based on the above assumption, a weighted linear combination of observation data is used to calculate future predictions. The weight decreases exponentially with the further increase of past observations. The smallest weight is associated with the oldest observations.According to the number of smoothing times, exponential smoothing methods include single exponential smoothing, second exponential smoothing, and third exponential smoothing. Single exponential smoothing is suitable for predicting data without clear trends or seasonal patterns. The second exponential smoothing is suitable for predicting the future value of the data with a linear trend. The third exponential smoothing is appropriate for the data with an obvious seasonal pattern. Due to the lack of clear trends and seasonal patterns in the observed data during equipment faults, single exponential smoothing is chosen as the basis for the algorithm. The future prediction is defined as:The smoothing parameter is often selected based on experience. However, few works are applied to predict the fault time of equipment. Therefore, there is no empirical value for reference. Besides, as time goes by, the distribution of random variables may change. If The classic exponential smoothing method takes the weighted average of the observed data as the future prediction. Therefore, when the observed values fluctuate within a small range of the average value, the exponential smoothing method presents a small error. However, if the variance of the observed values is large, the single exponential smoothing method may cause significant errors at certain time points.The analysis shows that the main reason for the higher local error is that the weighted average of the observations is directly used as the prediction result. The problem can be solved by establishing a mapping between the weighted average of the observations and the future prediction. We also use the weighted average value as the independent variable to fit the future prediction.Assume that there is a mapping between the weighted average and the future prediction of the observations such that mentclass2pt{minimFrom Fig. Combining Eq. , we can If Eq.  satisfieThen, this neural network model is equivalent to the single exponential smoothing model. However, if the activation function Equation  is the ps in Eq. . AssuminBesides, the weights n in Eq. . TransfoDue to the equality constraint of Eq. , the bacTaking the partial derivatives of Eq.  concerniThe partial derivatives of In Eq. , the parThe 8-year (2010–2018) maintenance data of the TR100  is adopted as the test data. The test data is grouped according to the truck number, and the fault interval time is computed (the next maintenance time minus the previous maintenance time) to obtain the sequence of time between faults. The data has been tested for stationarity, and the results indicate that the test data is a non-stationary sequence.The ESNN algorithm is implemented in C# language and executed in Visual Studio 2013. All experiments are performed on the same workstation .For the time series of each truck's fault interval, ESNN and Autoregressive Integrated Moving Average (ARIMA) are used to predict the time between truck faults under four different sliding window sizes (win size). Among them, ARIMA belongs to the classic model in time series analysis, which is widely used due to its high prediction efficiency, small error, and other advantages, and it also conforms to the prediction scenario. Repeat the experiment ten times under each sized sliding window and average the obtained results to produce the final output. In addition, five performance indicators are applied in the experiment: absolute error (AE), relative error(RE), Akaike’s information criterion(AIC), corrected Akaike’s information criterion(AICc), and Schwarz’s Bayesian information criterion(BIC). These five information errors are divided into two categories: traditional metrics (AE and RE) and information criterion metrics . Traditional metrics measure the predictive performance of the model, while the information criteria are used to evaluate the fitting effect of the model.The testing for stationarity shows that the test data is the non-stationary series. Therefore, the differential order of ARIMA is set as 0. The parameters of ESNN are described in Table In the experiment, we chose sliding window sizes of 6, 7, 8, and 9. They meet the size of the data in this paper and improve prediction accuracy.Figure Table We analyze the principle of the exponential smoothing model and the problems in predicting the truck's fault time, and design a neural network model based on single exponential smoothing method. Compared with existing state-of-the-art methods, ESNN achieves higher accuracy and provides an effective approach for predicting the truck's fault time in open-pit mines.By adjusting the ESNN parameters, we obtain an optimal set of parameters suitable for predicting the fault time of open-pit mines trucks, which provides reliable information for enterprises to develop preventive maintenance plans.In the future, we will improve the nonlinear exponential smoothing algorithm to better adapt to the neural network model.In the future, we will reduce the complexity of neural networks to improve prediction accuracy and applicability to higher latitude data.Although the ESNN algorithm has better performance compared to other algorithms, it still has some limitations. For example, the ESNN results are not very prominent for some experimental sliding windows. Due to the structural characteristics of nonlinear exponential smoothing algorithms, the optimized neural network may have a curse of dimensionality in complexity, which makes ESNN unsuitable for predicting long-term fault problems. Therefore, we will conduct further research on these issues in the future, as follows:


The optimal scheduling of energy in a smart grid is crucial to the energy consumption of the entire grid. In fact, for larger grids, intelligent scheduling may result in substantial energy savings. Herein, we introduce an enhanced dynamic programming algorithm (DPA) that utilizes two state variables to derive the optimal power supply schedule. The algorithm accounts for the dynamic states of both batteries and supercapacitors in the power supply system to augment the performance of the dynamic programming model. Additionally, this study incorporates a long short-term memory (LSTM) deep learning model, which integrates various environmental factors such as temperature, humidity, wind, and precipitation to predict grid power consumption. This serves as a mid-point pre-processing step for smart grid energy consumption scheduling. Our simulation experiments confirm that the proposed method significantly reduces energy consumption, surpassing similar grid energy consumption scheduling algorithms. This is critical for the establishment of smart grids and the reduction of energy consumption and emissions. With the continuous evolution of the electric power industry, the conventional power grid system is no longer suited for the current stage of power demand. Power supply enterprises must now ensure not only adequate but also high-quality power supply . Power sThe earliest power system energy scheduling problems were solved using traditional mathematical methods. For instance, As the electric power industry continues to grow, power generation systems have become increasingly complex, with nonlinearity, high dimensionality, and intricate constraints. Recently, computer and artificial intelligence techniques have been introduced to automate the solution of some of these complex problems . VariousThe problem of optimal scheduling of energy consumption in smart grids is of great significance for improving grid efficiency and conserving fossil energy. With the development of artificial intelligence, many intelligent algorithms are currently being used to solve the energy dispatching problem in power grids. However, traditional mathematics-based methods and some simple intelligent algorithms  are not Most machine learning algorithms for intelligent scheduling do not consider power consumption forecasting. Electricity consumption prediction parameters can be used to provide guidance for intelligent scheduling of electricity consumption to greatly reduce energy consumption losses. Time series prediction models are a class of models used in deep learning to solve similar problems. RNN , as the The problem is solved through the implementation of a DPA, which involves the subdivision of the problem into defined states and the establishment of relationships between these states. Over the years, numerous scholars have refined this algorithm to accommodate a diverse range of problem domains. In this article, we propose a smart grid energy scheduling algorithm based on the improved DPA algorithm and LSTM. The specific contributions are as follows:(1) Environmental variables, including temperature, humidity, wind and precipitation, are used to determine the characteristics of power grid energy consumption.(2) LSTM is used to predict power grid energy consumption as one of the conditions of energy scheduling.(3) An improved DPA algorithm with two state variables is proposed, which lists both battery and supercapacitor as state variables in the smart grid.To address the energy consumption scheduling problem in smart grids and maximize energy savings, this study proposes an energy consumption scheduling method that employs an enhanced DPA. However, it is crucial to note that the entire energy consumption scheduling process is not solely executed using the DPA. Rather, it relies on the accurate prediction of electricity consumption by a node in the future period. LSTM has found extensive application in natural language processing, where it addresses the long dependency problem in model training compared to RNN. The LSTM process is roughly divided into three steps. The first step is to determine what information to discard from the cell state, which is done through the oblivion gate. The second step is to determine what information is stored in the cell state, which is determined by the input gate. Finally, determine which information to output, which is determined by the output gate result and the cell state. However, to enable accurate prediction of energy consumption of a node in the power grid, LSTM requires ample data and an appropriate experimental setup. In reality, the variation of grid energy consumption is not only linked to the load but also to the environment where the grid is situated. To this end, this study collects the daily average data of wind speed, precipitation, temperature, and humidity in a city and trains them simultaneously with daily electricity consumption. Specifically, wind speed, precipitation, temperature, and humidity are input as influencing features of electricity consumption to the LSTM model. In this article, four kinds of environmental data of T City in 2020 and data of a node of the power grid are collected and organized in the form of matrix. The input feature structure is depicted in via the DC converter. The Rint model is used for the battery, and the internal resistance model is employed for the supercapacitor, with the internal resistance of both remaining constant. In conventional states, single state variables can describe state characteristics, but in smart power grids, the states of batteries and supercapacitors are equally important, so single state variables cannot fully describe their changes.The initial step towards addressing the smart grid energy consumption dispatching issue through dynamic programming is to develop a model for the energy storage system of the smart grid. The fundamental configuration of the smart grid energy storage system is displayed in k discrete event interval is Preq(k), then the energy flow relationship of its on-board hybrid energy storage system is shown in Assuming that the power demand of a node of the smart grid at the Ub(k) is the battery no-load voltage, Ib(k) is the battery current, Usc(k) is the supercapacitor no-load voltage, Isc(k) is the supercapacitor current; η is the efficiency of the DC converter; Preq(k) > 0 indicates the smart grid in operating state; Preq(k) < 0 indicates the smart grid in energy-saving state.where SOC), and the following functional relationship is obtained by conducting experiments on the hybrid power pulse characteristics of the battery monomer, performing parameter discrimination and fourth-order polynomial fitting: The no-load voltage of the battery is considered as a function of the battery charge state  is the charge state of the battery at the k-th discrete time interval.where, Redefining the charge state of a supercapacitor as the ratio of the square of the voltage value in a certain state to the square of the voltage value in a fully charged state, the function of the no-load voltage of a supercapacitor with respect to its charge state is as follows: Usc(k) is the voltage of the supercapacitor under full charge; SOCsc(k) is the charged state of the ultracapacitor at the k discrete time interval.where, The concept of state variables pertains to variables that articulate the inherent state of a process at the outset of each stage within a DPA. Furthermore, the input state variable in each stage is contingent on the output state variable of the preceding stage. While the traditional DPA employs single-state variables to represent the process, such an approach imposes certain limitations in adequately capturing the intricacies of electricity consumption scheduling within the framework of a smart grid. To address this issue, our article proposes the use of dual-state variables to enhance the effectiveness of the DPA.SOC of battery and supercapacitor are taken as the state variables of the system z1(k) and z2(k) , and the decision variable j(k) is taken as the output power of battery Pb(k), then the output power of supercapacitor can be determined as: The grid energy consumption scheduling process is deemed a problem-solving process, which is segmented into n stages based on the time sequence of the demanded power during system operation. In the context of smart grid energy consumption scheduling, the state of charge of energy storage devices, namely, the battery and supercapacitor, serves as an indicator of the entire system’s state. Therefore, the k + 1-th phase are determined by the state variables and decision variables in the k-th phase. The discrete situation of the state equation of the smart grid energy storage system is shown in f1 and f2 are the transfer functions for the state variables z1(k) and z2(k), respectively.where t(k) indicates the duration of the k phase; Qb,N is the rated capacity of the battery; Csc,N is the rated capacitance of the supercapacitor; and Usc,N is the rated voltage of the supercapacitor.where, Δn-th stage is: The expression of the optimal objective function for a certain state variable at the L represents the cost function, which is the loss value of the smart grid energy storage system.where k-th phase, when the state variables are determined, is shown in k-th state to the last state process. The optimal objective function used to determine the optimal decision in the z1(k) and z2(k) are the state variables of smart grid, and u(k) isthe decision variable.where This article’s experimental setup is bifurcated into two distinct segments. The first segment aims to substantiate the efficacy of the proposed LSTM-based electricity consumption prediction method that factors in environmental considerations. Specifically, in this section, we compare the performance of the time series model and the degree of fit of the prediction curve for the method considering environmental factors against the one that does not. The second segment of the experiment involves the evaluation of the overall smart grid energy scheduling system for energy conservation. To this end, we compare the performance of comparable algorithms using various indices to validate the efficacy of the DPA that incorporates the improved two-state variables proposed in this article. The experimental environment comprised an Intel(R) Xeon(R) CPR E5-2620 @ 2.40 GHz processor with 64G running memory and NVIDIA Tesla V100 GPU, whereas the software environment involved Pytorch 1.11.0 under the Ubuntu 20.0.4 operating system.The following are the evaluation metrics used to assess the LSTM approach that incorporates environmental factors: (1)MAPE (Mean absolute percentage error), sensitive to relative error, does not change due to global scaling of the target variable:  (2)MSE (Mean squared error), the mean of the absolute squared errors of the predicted and true values: This experiment aims to validate the effectiveness of using LSTM models for electricity consumption prediction after integrating environmental factors. Specifically, the experiment involves training a model that combines environmental variables and comparing its performance against a model trained using only electricity consumption data as input. The comparison of the two models’ performance in terms of evaluation metrics is presented in The experimentation was conducted using MATLAB , where a total of ten generation modules were simulated. Ablation experiments were devised to test the efficacy of the proposed smart grid energy consumption scheduling system’s two components. Firstly, the dynamic programming scheduling algorithm was omitted, and secondly, the traditional DPA was used for scheduling optimization. Thirdly, the two-state variable improved DPA was employed, and fourthly, the two-state variable DPA was combined with the LSTM electricity consumption prediction method, taking into account environmental factors. Comparison experiments were also conducted to validate the overall system’s effectiveness, where the particle swarm optimization algorithm, the ant colony algorithm, and the random forest algorithm were compared to the same type of energy consumption scheduling algorithms.Based on the results of the ablation experiments conducted on the time series model, the LSTM method incorporating environmental factors outperforms the original LSTM and RNN, and even surpasses the CNN-LSTM, which has been previously shown to outperform the LSTM. These findings highlight the significance of incorporating multiple factors in grid electricity consumption prediction. In terms of the overall scheduling algorithm, adding the LSTM incorporating environmental variables and the two-state variable DPA have varying degrees of performance improvement for the overall algorithm, and the effectiveness of both algorithms has been demonstrated in this article. The final comparison experiments confirm that the whole system can reduce energy consumption to varying degrees for different power generation modules, and the performance of energy consumption reduction is similar, with even more energy consumption reduction achieved in the lowest power module 5. These results underscore the universality and practical significance of the method presented in this article.In this article, we have presented a novel approach for energy consumption scheduling in smart grids by utilizing the LSTM algorithm and an improved DPA. Our approach incorporates environmental factors as input variables for power consumption prediction and introduces a dual-state variable to enhance the DPA’s effectiveness. The results have demonstrated that the inclusion of environmental factors can lead to better fitting of real power consumption curves, and the improved DPA with dual-state variables can provide better energy consumption scheduling and energy savings for smart grids. The application of our method can effectively reduce energy consumption in the grid, thus contributing to energy conservation and environmental protection.Future work can focus on incorporating more environmental factors, including not only natural factors but also factors related to grid equipment, into the grid electricity consumption prediction. Additionally, further improvements can be made to the dynamic programming process beyond the consideration of state variables alone. In addition, we plan to gather more comprehensive data and carry out feature extraction through different deep learning models to verify the generalization of the method.10.7717/peerj-cs.1482/supp-1Supplemental Information 1Click here for additional data file.