Accuracy Of Information Processing Research Articles

Quantitative data processing of whole service value chain for power grid internal simulation market

The internal simulation market system can stimulate employee initiative, reduce costs and improve information processing efficiency. However, the complexity of the internal simulation market poses a challenge to computing resources. Efficient data processing techniques are crucial for internal simulation market systems. In this paper, the internal simulation market model and the value chain theory are first put forward. Second, the internal simulation market construction within power grid enterprises is proposed. Then we propose a data mining-based collaborative fusion and processing method for multi-value chain quantitative data in power grid internal simulation markets to conduct data processing, including data fusion, abnormal data elimination and data dimensionality reduction, thereby improving the accuracy of information processing and data sharing. Finally, we validate the superior performance of the proposed method through simulations.

Enhancing accuracy of information processing in onboard subsystems of UAVs

The object of research is the onboard subsystems of Unmanned Aerial Vehicles (UAVs). The research is aimed at analyzing UAVs, specifically the integration and enhancement of satellite-based positioning systems, including Global Navigation Satellite Systems (GNSS) and Inertial Navigation Systems (INS). The problem concerns traditional satellite-based positioning services, especially those relying solely on medium earth orbit (MEO) satellites, which are insufficient for specific requirements. The study aims to address the limitations of these systems on onboard subsystems of UAVs, especially in challenging environments laden with jammers and interference, and to provide a more accurate, robust, and continuous positioning solution. The research proposes a «multilayer system of systems» approach that integrates signals from various sources, including low Earth orbit (LEO) satellites, ground-based positioning, navigation, and timing (PNT) systems, and user-centric sensors. The combined approach, termed LeGNSS/INS, leverages the strengths of each component, providing redundancy and enhanced accuracy. The system's performance was evaluated using pseudo-real output data, demonstrating its ability to generate quasi-real dynamic trajectories for UAV flight. The error analysis showed that the proposed method consistently outperforms traditional GNSS systems, especially in challenging environments. The enhanced performance of the LeGNSS/INS system can be attributed to integrating multiple satellite systems with INS and applying optimal filtering techniques. The research also employed mathematical modeling to represent the dependencies and interactions when combining data from different sources, such as GPS, LEO, and INS. The Kalman filter is a mechanism to fuse data from multiple sources optimally. The insights from this study apply to various sectors, including aviation, maritime navigation, autonomous drones, and defense. The enhanced positioning accuracy can significantly improve safety, navigation precision, and operational efficiency. However, the study assumes idealized conditions for satellite signal reception, which might not always be accurate in real-world scenarios. Challenges, such as the martial law conditions in Ukraine affecting data collection and potential satellite signal restrictions, were also highlighted. Further research can delve into the impact of more complex environmental factors and the integration of additional satellite systems or sensors to enhance accuracy further.

Research on computer network information image data processing based on machine learning

Abstract In this paper, we aim to progress the efficiency and accuracy of information processing and security detection in computer networks by introducing convolutional neural networks in machine learning algorithms that are capable of multi-scaling from the channel attention module and spatial attention module in extracting image information. Global maximum pooling and global averaging are done for the feature maps generated by both modules to get the clearest feature maps by dimensionality reduction. The loss function is used to calculate the feature maps to reduce the data loss generated during data extraction and finally complete the image data processing. To verify the effectiveness of the proposed platform, network images containing different amounts of data are input into the platform, and the accuracy and loss of data extraction are obtained. The results show that the data extraction accuracy of the reduced platform is up to 100%, which is 6% higher than other platforms. The number of data losses in other platforms is more than twice of this paper, while the number of losses in this paper can be controlled within 5. It can be seen that convolutional neural network in machine learning improves the accuracy of data extraction from computer network information images and reduces the loss in data extraction.

The differences between cerebral dominance patterns in the speed and accuracy of information processing among university students

IntroductionThe subject of cerebral dominance has received great attention by researchers; however, there is a paucity of studies that have examined its relationship to information processing. ObjectiveThe current study aims to reveal the differences between the cerebral dominance patterns (left cerebral dominance, right cerebral dominance, integrated parallel cerebral dominance) in the speed and accuracy of information processing. MethodA sample of (182) volunteers university students, (mean age±SD, 19.92±2.76 years; range, 18–24years; 68 male, 114 females). The Human Information processing survey was applied to the participants. Each subject was exposed to two elementary cognitive tasks (Stroop task and Eriksen flanker task). Each task included two experimental conditions: first, the congruent condition that reflects the automatic processing of information; second, the incongruent condition that reflects the controlled processing of information. ResultsThe results of the study revealed that there were no differences between the patterns of cerebral dominance in the speed and accuracy of information processing. ConclusionAlthough the relationship between cerebral dominance and information processing is not significant, we need more studies to examine this relationship in other samples and in other experimental conditions.

Wealth dynamics in a market with information asymmetries.

Financial markets are usually investigated through time series such as prices and volumes that describe the behavior of the system as a whole. Such observables emerge from microeconomic interactions between market participants. Agent-based models have been utilized to shed light on this process. The model's ability to produce statistics frequently found in empirical data is evidence of some correspondence with real markets. Here, an agent-based market model is proposed. Different trader profiles with short- and long-term motivations are considered, and limitations on the agents' skills to manipulate information are inserted into the model. According to their profile and limitations, agents are rational. A differential equationapproximation is employed to find the value to which the price converges and the timescale of this process. The relationship between agents' attributes and the evolution of their wealth is explored in different scenarios. Agent's average wealth was not significantly affected by information processing accuracy, but the standard deviation was. The increased risk is the main consequence of low accuracy. The model yielded price series with multifractal behavior and heavy-tailed return distributions, which are nontrivial statistics frequently observed in empirical series.

Оценка показателей качества технических средств автоматизированных систем управления

The aim of the study is to show the necessity to solve the problem of assessing the quality indicators of the automated systems facilities; to demonstrate that the work quality problem of system facilities is closely connected with choosing rational technologies for processing information in the system. The article is devoted to calculating the main quality indicators of operating the automated system facilities, which include the time to complete the system tasks, the output information accuracy and the system feasibility. Research methods are statistical analysis of the operating quality of the automated system facilities using the methods of probability theory and parallel data processing. The novelty of the work is using methods of parallel data processing in calculating the main quality indicators of operating the automated control system facilities. The study results in developing a methodology for assessing the quality of the automated system facilities, namely the time to complete the system tasks, the output information accuracy and the system feasibility. The article concludes that the methods given in the article for calculating the main quality indicators of operating the automated system facilities make it possible to increase the efficiency of automated control systems by boosting the reliability and accuracy of information processing

Traditional and perspective systems of maintenance and repair of traction rolling stock

Various systems of maintenance and repair of traction rolling stock have been studied using the example of multiple unit rolling stock, and their comparison has been made. The analysis of existing, historically formed systems of preventive maintenance, and perspective maintenance and repair systems based on technical diagnostics data is carried out.As an example, the COMPACS® system is considered, which is a universal system for complex monitoring of the technical condition of equipment, which has a flexible distributed parallel-serial architecture that provides high speed and accuracy of information processing. The main advantage of COMPACS® is the availability of an automatic expert decision support system. The expert system is designed for automatic diagnostics and prediction of the technical condition of the equipment. The task of the expert system is to assist the personnel in making the necessary informed decisions on the management of the operating mode and the state of the equipment. The system of work is described, as well as the scope and principle of operation of this complex monitoring system.The author comes to the conclusion that it is necessary to create an integrated repair system with a scientifically substantiated regulatory effect on equipment, taking into account the actual technical condition of the units and assemblies of the electric locomotive.

Associations between digital amnesia, sleep disorders and somatic symptoms among youth

Digitalization has accelerated and improved the accuracy of information processing. It has changed society into a connected world. The spread of COVID-19 changed individuals' daily routines drastically over the past two years, especially among youth due to the temporary closure of colleges and universities. The usage of digital devices by youth has increased as a result of online learning platforms, leading to digital reliance and a negative impact on their sleep quality. Therefore, this study aimed to investigate (1) the association between digital amnesia, sleep disorders and somatic symptoms, (2) the difference in digital amnesia based on the demographic categories, (3) the effect of digital amnesia and sleep disorders on somatic symptoms. A cross-sectional study was conducted on 326 young people aged 18 to 25 years. Data was collected from participants using the Digital Amnesia Scale, Sleep Disorders Symptom Checklist (SDS-CL-17), and Somatic Symptom Disorder-B Criteria Scale (SSD-12). There was a significant association between digital amnesia, sleep disorders, and somatic symptoms among youth. Youth differed significantly in their digital amnesia based on their demographic categories such as gender and family type. Somatic symptoms among youth were significantly impacted by digital amnesia and sleep disorders. Youth could overcome their digital amnesia by utilizing digital devices effectively. Youth could also be sensitized about adverse effects of digital amnesia. Taking a digital break could help them improve their quality of sleep and reduce somatic symptoms.

The Age-Related Changes in Speed of Visual Perception, Visual Verbal and Visuomotor Performance, and Nonverbal Intelligence During Early School Years.

Speed of sensory information processing has long been recognized as an important characteristic of global intelligence, though few studies have concurrently investigated the contribution of different types of information processing to nonverbal IQ in children, nor looked at whether chronological age vs. months of early schooling plays a larger role. Thus, this study investigated the speed of visual information processing in three tasks including a simple visual inspection time (IT) task, a visual-verbal processing task using Rapid Automatic Naming (RAN) of objects as an accepted preschool predictor of reading, and a visuomotor processing task using a game-like iPad application, (the “SLURP” task) that requires writing like skills, in association with nonverbal IQ (Raven’s Coloured Progressive Matrices) in children (n = 100) aged 5–7 years old. Our results indicate that the rate and accuracy of information processing for all three tasks develop with age, but that only RAN and SLURP rates show significant improvement with years of schooling. RAN and SLURP also correlated significantly with nonverbal IQ scores, but not with IT. Regression analyses demonstrate that months of formal schooling provide additional contributions to the speed of dual-task visual-verbal (RAN) and visuomotor performance and Raven’s scores supporting the domain-specific hypothesis of processing speed development for specific skills as they contribute to global measures such as nonverbal IQ. Finally, RAN and SLURP are likely to be useful measures for the early identification of young children with lower intelligence and potentially poor reading.

Measuring the Uncertainty in the Original and Negation of Evidence Using Belief Entropy for Conflict Data Fusion.

Dempster-Shafer (DS) evidence theory is widely used in various fields of uncertain information processing, but it may produce counterintuitive results when dealing with conflicting data. Therefore, this paper proposes a new data fusion method which combines the Deng entropy and the negation of basic probability assignment (BPA). In this method, the uncertain degree in the original BPA and the negation of BPA are considered simultaneously. The degree of uncertainty of BPA and negation of BPA is measured by the Deng entropy, and the two uncertain measurement results are integrated as the final uncertainty degree of the evidence. This new method can not only deal with the data fusion of conflicting evidence, but it can also obtain more uncertain information through the negation of BPA, which is of great help to improve the accuracy of information processing and to reduce the loss of information. We apply it to numerical examples and fault diagnosis experiments to verify the effectiveness and superiority of the method. In addition, some open issues existing in current work, such as the limitations of the Dempster-Shafer theory (DST) under the open world assumption and the necessary properties of uncertainty measurement methods, are also discussed in this paper.

Weighted Adaptive Partition for Heterogeneous IoT Data Stream

In the past, many researchers used sliding-window mechanism in Internet of Things (IoT) data stream mining, which can divide the incoming large volume data into small segments and improve the information processing rate. With the emergence of heterogeneous IoT data streams, data forms become richer and more diverse. The fusion analysis of multimodality information can improve the accuracy of information processing and network decision-making, because it can make up for the lack of some information in a single-mode data stream. However, the representation of information in different data sources is different, and the amount of information contained in it is distinct. Thus, a weighted adaptive partition algorithm is proposed in this article, which performs a dynamic weighted fusion of multisource heterogeneous IoT data in sliding windows. The article adopts the Gaussian mixture model (GMM) to adaptively determine the weights of each data source, which is calculated based on the judgement confidence of each modal data to the change in data stream. Besides, the probability distribution change detection mechanism based on multimodality restricted Boltzmann machine (multimodality RBM) is proposed, which measures the joint probability distribution by the weighted data’s energy value. Finally, according to the energy change rate of the data stream, the size of the window is adjusted adaptively to realize the partition for heterogeneous IoT data stream. The algorithm proposed in this article is verified on the CUAVE data set and the analysis of results shows that the data under adaptive weighted fusion has better performance.

The Effectiveness of Psychosocial Program on Speed and Accuracy of Information Processing in Students with Sluggish Cognitive Tempo (SCT)

The Effectiveness of Psychosocial Program on Speed and Accuracy of Information Processing in Students with Sluggish Cognitive Tempo (SCT)

Affective symptoms across the life course and resilience in cognitive function

Background: Little is known about what factors can modify the relationship between affective symptoms and cognitive function across the life course.Aim: To investigate a number of factors that can contribute to resilience in cognitive function in relation to affective symptoms, using data from the National Child Development Study.Subjects and methods: Adult affective symptoms were measured using the Malaise Inventory Scale (ages 23, 33, 42 and 50). Measures of immediate and delayed memory, verbal fluency and information processing accuracy (age 50) were used to derive measures of resilience in cognitive function—better than predicted cognition, when accounting for experiences of affective symptoms. Factors contributing to resilience in cognitive function were informed by a literature review and included sex, childhood cognitive ability, education, household socio-economic position (SEP), midlife SEP, and APOE genotype. Linear regression and structural equation modelling approaches were used for analyses.Results: Higher childhood cognitive ability, educational level, midlife SEP and female sex contributed to better than predicted cognitive function in relation to affective symptoms (i.e. resilience), with particularly consistent effects for memory. No effects on resilience were revealed for APOE genotype.Conclusion: Understanding factors contributing to resilience in cognitive function in those with affective symptoms can inform interventions to promote healthy cognitive ageing for those at risk.

LOGIC THRESHOLD FOR MICRORING RESONATOR-BASED BDD CIRCUITS: PHYSICAL AND OPERATIONAL ANALYSES

Moore’s Law has been the fuel of expansive innovation in computing. The chip industry kept the Moore’s law extant for almost four decades. However, the halt of the rapid progress of the silicon technology is incipient by reason of the physical limitations. Emerging computing proposals suggest several alternatives to current computing paradigms and technologybases. The photonic circuitry is one of the most promising candidates with its high operation speed, energy efficient passive components, low crosstalk and appropriateness for parallel computation. Among various approaches to photonic logic, microring resonator-based Binary-Decision Diagram (BDD) architectures have a special place due to their small circuit footprint. However, the physical limitations imposed on their logic implementation has not been studied in depth to enable design of efficient circuits. In this paper, we study the physical structure and operational details of a microring resonatorbased Half-Adder (HA) circuit and outline the conditions under which the performance and accuracy of information processing is compromised due to its physical characteristics. Our analyses significantly contribute to determining key physical features and operations concerning logic implementation of microring resonator based BDD HA, which informs the future design and operational optimization of the microring resonator-based BDD logic circuits.

Motor reaction time and accuracy in patients with multiple sclerosis: effects of an active computerized training program.

In this study, we aimed to determine the difference of motor reaction time and accuracy between the patients with moderate multiple sclerosis (MS) and healthy subjects and to determine whether a specified cognitive-motor training can improve the speed and accuracy of information processing in patients with MS. A total of 205 patients (30% males) and 276 age-/gender-matched healthy subjects (31% males) were included in the study. Furthermore, using a stratified randomization method, patients with MS were randomly assigned to one of two groups: active information processing training (AIPT) (n = 49) or post-control (n = 55). The AIPT group was asked to complete a computerized visual-manual training program and the post-control group asked to complete the same task without an increase in difficulty. Before and after the intervention phase, the simple, choice, and semantic reaction times and accuracies of all participants were evaluated using the VLS measurement battery. Our results demonstrated that the case and control group were significantly different in terms of the simple, choice, and semantic motor reaction times and accuracies. Compared with the pre-intervention phase, the AIPT and control group's performances in the post-intervention phase were considerably improved in simple, choice, and semantic motor reaction times and choice and semantic motor reaction accuracies. The results also showed that the AIPT group performed significantly better than the post-control group in terms of simple and choice motor reaction times. We showed the positive effects of training on the performance of patients with MS in motor reaction time and accuracy.

Information-thermodynamics link revisited

The so-called information-thermodynamics link created by a thought experiment of Szilard became a core of modern orthodoxy in the field of quantum information and resources theory in quantum thermodynamics. We recall existing objections against standard interpretation of Szilard engine operation and illustrate them by two quantum models: a particle in a box with time-dependent thin potential barrier and the spin-boson model. The consequences of the emerging superselection rules for thermodynamics and foundations of quantum mechanics are discussed. The role of non-ergodic systems as information carriers and the thermodynamic cost of stability and accuracy of information processing is briefly discussed and compared to the generally accepted Landauer’s principle.

Longitudinal associations of affective symptoms with mid-life cognitive function: evidence from a British birth cohort.

Affective disorders are associated with poorer cognition in older adults; however, whether this association can already be observed in mid-life remains unclear. To investigate the effects of affective symptoms over a period of 30 years on mid-life cognitive function. First, we explored whether timing (sensitive period) or persistence (accumulation) of affective symptoms predicted cognitive function. Second, we tested how different longitudinal trajectories of affective symptoms were associated with cognitive function. The study used data from the National Child Development Study. Memory, verbal fluency, information processing speed and accuracy were measured at age 50. Affective symptoms were measured at ages 23, 33, 42 and 50 and used to derive longitudinal trajectories. A structured modelling approach compared a set of nested models in order to test accumulation versus sensitive period hypotheses. Linear regressions and structural equation modelling were used to test for longitudinal associations of affective symptoms with cognitive function. Accumulation of affective symptoms was found to be the best fit for the data, with persistent affective symptoms being associated with poorer immediate memory (b = -0.07, s.e. = 0.03, P = 0.01), delayed memory (b = -0.13, s.e. = 0.04, P < 0.001) and information processing accuracy (b = 0.18, s.e. = 0.08, P = 0.03), but not with information processing speed (b = 3.15, s.e. = 1.89, P = 0.10). Longitudinal trajectories of repeated affective symptoms were associated with poorer memory, verbal fluency and information processing accuracy. Persistent affective symptoms can affect cognitive function in mid-life. Effective management of affective disorders to prevent recurrence may reduce risk of poor cognitive outcomes and promote healthy cognitive ageing. None.

The Temporal Dimensions in the First Year of Life

Time is a multifaceted concept that is critical in our cognitive lives and can refer, among others, to the period that lapses between the initial encounter with a stimulus and its posterior recognition, as well as to the specific duration of a certain event. In the first part of this paper, we will review studies that explain the involvement of the temporal dimension in the processing of sensory information, in the form of a temporal delay that impacts the accuracy of information processing. We will review studies that investigate the time intervals required to encode, retain, and remember a stimulus across sensory modalities in preverbal infants. In the second part, we will review studies that examine preverbal infants’ ability to encode the duration and distinguish events. In particular, we will discuss recent studies that show how the ability to recognize the timing of events in infants and newborns parallels, and is related to, their ability to compute other quantitative dimensions, such as number and space.

The impact of metaphors on information visualization

Metaphor is the underlying mechanism of information communication. Although metaphors are ubiquitous in information visualization designs, different connotations influence users’ information processing differently. Visual metaphors, however, may imply interfering attributes possibly caused by the source concepts, and lead to backfire effects on users’ inferences. Understanding the pros and cons of metaphoric transfer effect in information visualization would help optimize visualization designs, and improve efficiency and accuracy of information processing. This paper empirically examines how metaphors influence different activities of information processing, including comprehension, inference and judgment in information visualization. Metaphors illustrating clear semantic meanings in the source domain can improve both the user’s conceptual fluency in information comprehension, and the rate of correct information search.

Innovative approach in the improvement of student’s attention

Integrated studies are aimed towards linking knowledge from a variety of subjects. By studying related subjects in an isolated manner the knowledge is acquired in a disorganized way. Therefore contemporary pedagogy demands the use of integrative teaching methodology to achieve the best results. An Integrative teaching methodology for children with learning difficulties and cognitive and motor disorders was used in 17 schools from different Latvian regions. Students acquired Integrative teaching methodology during one study year. Attention span and concentration abilities using Cognitron test from Vienna test system were estimated for 615 students before and after the acquiring of Integrative teaching methodology. The results of younger students have improved more than the results of older students after using this methodology. The largest increase of speed and accuracy of information processing was observed for 3-4 grade students of general schools.