Error Cost Research Articles

Edge-Enabled Blockchain-Based V2X Scheme for Secure Communication Within the Smart City Development

As the high mobility nature of the vehicles results in frequent leaving and joining the transportation network, real-time data must be collected and shared in a timely manner. In such a transportation network, malicious vehicles can disrupt services and create serious issues, such as deadlocks and accidents. The blockchain is a technology that ensures traceability, consistency, and security in transportation networks. In this study, we integrated edge computing and blockchain technology to improve the optimal utilization of resources, especially in terms of computing, communication, security, and storage. We propose a novel, edge-integrated, blockchain-based vehicle platoon security scheme. For the vehicle platoon, we developed the security architecture, implemented smart contracts for practical network scenarios in NS-3, and integrated them with the SUMO TraCI API. We exhaustively simulated all the scenarios and analyzed the communication performance metrics, such as throughput, delay, and jitter, and the security performance metrics, such as mean squared error, communication, and computational cost. The performance results demonstrate that the developed scheme can solve security-related issues more effectively and efficiently in smart cities.

РЕТРОСПЕКТИВНИЙ АНАЛІЗ ВАРТОСТІ ПОХИБКИ ПРОГНОЗУ ДЛЯ ПОБУДОВИ БАЛАНСУЮЧИХ ГРУП ВИРОБНИКІВ З ВІДНОВЛЮВАНИХ ДЖЕРЕЛ ЕНЕРГІЇ

The significant increase in the installed capacity of power plants with renewable energy sources and the imbalance of the financial system of the wholesale electricity market of Ukraine prompted the Ministry of Energy to develop an alter-native support mechanism for RES producers. The introduction of a feed-in tariff (FIP), which compensates for the difference between the actual sale price of electricity and the "green" tariff, can help producers receive more money immediately after the electricity is released. However, studies have shown that exiting a balancing group without form-ing a new one can lead to increased costs associated with forecasting error. It is important for manufacturers to form independent balancing groups to compensate for negative consequences. The findings of the article show that there is no single optimal balancing group for all manufacturers, but some groups are often repeated. Switching to a separate balancing group can have a significant economic effect for the manufacturer, reducing the cost of forecasting error compared to being solely responsible for the imbalance. However, the balancing group determined by the method of retrospective calculation of the cost of the forecast error is not stable in the long term. Ref. 8, fig. 2, tab. 3 Key words: renewable sources, electricity market, short-term forecasting, forecast interval, deep learning neural net-works, cost estimation.

Prescribed-Time Formation Control for a Class of Multiagent Systems via Fuzzy Reinforcement Learning

This paper concerns optimal prescribed-time forma- tion control for a class of nonlinear multi-agent systems (MASs). Optimal control depends on the solution of the Hamilton-Jacobi- Bellman equation, which is hard to be calculated directly due to its inherent nonlinearity. To overcome this difficulty, the rein- forcement learning strategy with fuzzy logic systems is proposed, in which identifier, actor, and critic are used to estimate unknown nonlinear dynamics, implement control behavior, and evaluate system performance, respectively. Different from the existing optimal control algorithms, a new performance index function considering formation error cost and control input energy cost is constructed to achieve optimal formation control of MASs within a prescribed time. The presented control strategy can ensure that the formation error converges to the desired accuracy within a prescribed time. Finally, the validity of the presented strategy is verified via a simulation example.

Finding the right one: Decision support for selecting cost-efficient order picking solutions

Enabled via recent technological advances coupled with the advent of new systems providers and decreased price points, automated and robotized order-picking solutions (e.g., pick-assisting autonomous mobile robots) have evolved as a surging market. Such innovative picking technologies aim to reduce labor costs, use available space more efficiently, and increase throughput rates. As implementation projects and the variety of solutions rise, managers decide which ones to select for their specific warehouse and products. However, comprehensive decision models for this strategic problem are lacking in the pertinent literature. We propose a mathematical optimization model for the novel problem that selects and sizes order-picking solutions and assigns them products and warehouse spaces. Expert interviews are used to identify the comprehensive decision-relevant costs and constraints. Specifically, we minimize setup, module, labor, and error costs while adhering to characteristics related to the area (e.g., available space), technology (e.g., throughput, handling capabilities of certain products), and product (e.g., physical dimensions). We conduct a case study and complement our findings with numerical experiments. We find significant cost reduction potential of up to 57% by selecting a mix of different order-picking solutions. Further analyses highlight the need to retain human workers and to account for maximum labor capacity.

Application Analysis of Artificial Intelligence Technology in Electrical Engineering Teaching

This article combines the characteristics of artificial intelligence technology and educational technology to realize the possibility of application in electrical automatic control systems, and applies artificial intelligence and educational technology to electrical engineering examples such as monitoring fire alarm open circuit design and smoke box appearance inspection in power transformation and distribution systems. Analyze the impact of artificial intelligence on electrical automation technology. The research results show that artificial intelligence and education can make electrical automation technology have the characteristics of simplification, distribution, informatization, diversity, flexibility, continuity, real-time and control complexity. Use artificial intelligence technology and educational technology to effectively analyze and process electrical engineering related parameters and data, reduce the use of lines and transformers, simplify work procedures, reduce error rates, save manpower, material resources and costs, and improve the accuracy of automatic control.

A Bayesian change-point detection approach to the economic evaluation of risky projects: an application to healthcare technology assessment

Abstract We propose a Bayesian hypothesis testing framework that allows for the assessment of evidence collected during a clinical trial about the cost-effectiveness of a healthcare technology. The model exploits a Bayesian updating rule that makes the link between the evidence collected in clinical research and the expected payoffs of adoption to the healthcare system. The framework takes into account the cost of decision errors in the payoff function, allowing the decision maker to compute the cost of taking a decision when evidence is far from the optimal decision triggers. We show, using a real-world cost-effectiveness study based on clinical trial evidence, how rules derived from a sequential adaptive design approach can lead to quicker decisions when compared to the value of information decision framework. Our application shows that a sequential approach has the potential to lead to quicker decisions, higher payoffs, and better health outcomes.

Uncovering personal circadian responses to light through particle swarm optimization

Background and objectivesKronauer's oscillator model of the human central pacemaker is one of the most commonly used approaches to study the human circadian response to light. Two sources of error when applying it to a personal light exposure have been identified: (1) as a populational model, it does not consider inter-individual variability, and (2) the initial conditions needed to integrate the model are usually unknown, and thus subjectively estimated. In this work, we evaluate the ability of particle swarm optimization (PSO) algorithms to simultaneously uncover the optimal initial conditions and individual parameters of a pre-defined Kronauer's oscillator model. MethodsA Canonical PSO, a Dynamic Multi-Swarm PSO and a novel modification of the latter, namely Hierarchical Dynamic Multi-Swarm PSO, are evaluated. Two different target models (under a regular and an irregular schedule) are defined, and the same realistic light profile is fed to them. Based on their output, a fitness function is proposed, which is minimized by the algorithms to find the optimum set of parameters and initial conditions of the model. ResultsWe demonstrate that Dynamic Multi-Swarm and Hierarchical Dynamic Multi-Swarm algorithms can accurately uncover personal circadian parameters under both regular and irregular schedules, but as expected, optimization is easier under a regular schedule. Circadian parameters play the most important role in the optimization process and should be prioritized over initial conditions, although assessment of the impact of misestimating the latter is recommended. The log-log linear relationship between mean absolute error and computational cost shows that the number of particles to use is at the discretion of the user. ConclusionsThe robustness and low errors achieved by the algorithms support their further testing, validation and systematic application to empirical data under a regular or irregular schedule. Uncovering personal circadian parameters can improve the assessment of the circadian status of a person and the applicability of personalized light therapies, as well as help to discover other factors that may lie behind the interindividual variability in the circadian response to light.

A density-based oversampling approach for class imbalance and data overlap

In data mining classification, class imbalance is characterized that different classes have an obvious difference in the number of samples. Most classifiers typically assume a balanced class distribution or assign equal classification error costs to different classes. Therefore, directly using imbalanced class will worsen the classification performance. The oversampling algorithms can achieve the balance by synthesizing new samples, but the uncontrollable positions of the synthetic samples may aggravate the data overlap and further deteriorate the classification performance. To tackle this challenge, an improved synthetic minority oversampling technique based on kernel density estimation and neighbor density selection (KDENDS_SMOTE) is proposed in this paper. First, each sample is mapped into a high-dimensional space to avoid the choice of the window width and to overcome the nonlinear separable limitation. Kernel density estimation is then used to derive the density ratio, which serves as a measure of the degree of data overlap. Subsequently, the stability degree of the density ratio is calculated using neighbor information, and a scoring mechanism combining the density ratio and its stability degree is proposed to assess the fitness of selected samples. Furthermore, the neighbor density selection based on the above scoring mechanism can guide SMOTE to generate new samples within a safe and stable region, away from areas with data overlap. Finally, compared with six advanced oversampling methods on fifteen real-world datasets, the KDENDS_SMOTE can effectively mitigate the data overlap and improve the classification performance.

Analyzing Polytomous Test Data: A Comparison Between an Information-Based IRT Model and the Generalized Partial Credit Model

Item response theory (IRT) models the relationship between the possible scores on a test item against a test taker’s attainment of the latent trait that the item is intended to measure. In this study, we compare two models for tests with polytomously scored items: the optimal scoring (OS) model, a nonparametric IRT model based on the principles of information theory, and the generalized partial credit (GPC) model, a widely used parametric alternative. We evaluate these models using both simulated and real test data. In the real data examples, the OS model demonstrates superior model fit compared to the GPC model across all analyzed datasets. In our simulation study, the OS model outperforms the GPC model in terms of bias, but at the cost of larger standard errors for the probabilities along the estimated item response functions. Furthermore, we illustrate how surprisal arc length, an IRT scale invariant measure of ability with metric properties, can be used to put scores from vastly different types of IRT models on a common scale. We also demonstrate how arc length can be a viable alternative to sum scores for scoring test takers.

Estimation Error Cost Optimization of Demand Side Management Based on Massive MIMO Communication Network in Smart Grid

The estimation error cost (EEC) of smart grid under the demand side management is related to the network behavior and the accuracy of energy dispatch. In this paper, a communication and energy bidirectional link connection network (CEBCN) is proposed for decreasing the EEC of smart grid under demand side management. In the CEBCN, the network behavior is quantified to communication reliability and response validity. And the communication, implemented by massive multi-input-multi-output (MIMO), and energy dispatch can be realized, simultaneously. Also, the EEC is further decreased through a cost-priority based energy dispatch strategy proposed in this paper. Based on the CEBCN, the closed-form expressions are derived about the communication reliability and response validity. Naturally, the closed-form solution is derived for the EEC in the communication and energy transmission model, the results of which are used to investigate the EEC performance in smart grid. Numerical results validate that the reliability and validity of the proposed CEBCN and reveal the effect of various parameters on the EEC. Notably, the high EEC caused by communication instability can be reduced by at least 40% with the massive MIMO, and the cost-priority based energy dispatch strategy can further improve the EEC performance on communication infrastructure.

A parallel fractional explicit group modified AOR iterative method for solving fractional Poisson equation with multi-core architecture

This research studies the Multi-core Architecture for the Modified Accelerated Overrelaxation (MAOR) scheme for solving the fractional Poisson equation. The equation is discretized using the fractional explicit group (FEG) finite difference method. This research also presents the parallel implementation of the fractional order iterative methods with the chessboard (CB) ordering strategies. The experimental results of the test problems were compared with other well known relaxation schemes to test their feasibility on parallel environment. The parameters that is measured include number of iteration, error, execution time and computational cost for various number of processors.

Product of bi-dimensional VAR(1) model components. An application to the cost of electricity load prediction errors

Abstract The multi-dimensional vector autoregressive (VAR) time series is often used to model the impulse-response functions of macroeconomics variables. However, in some economical applications, the variable of main interest is the product of time series describing market variables, like e.g. the cost, being the product of price and volume. In this paper, we analyze the product of the bi-dimensional VAR(1) model components. For the introduced time series, we derive general formulas for the autocovariance function and study its properties for different cases of cross-dependence between the VAR(1) model components. The theoretical results are then illustrated in the simulation study for two types of bivariate distributions of the residual series, namely the Gaussian and Student’s t. The obtained results are applied for the electricity market case study, in which we show that the additional cost of balancing load prediction errors prior to delivery can be well described by time series being the product of the VAR(1) model components with the bivariate normal inverse Gaussian distribution.

An adaptive algorithm for voice quality based on big data voiceprint identification

Abstract This paper improves the speech amplitude in Bayesian speech enhancement estimation by introducing a super-Gaussian cardinality distribution probability density function in the filter's construction. The derivation is combined with the perceptual error function, the new probability density function and the perceptual error cost function to better exploit and utilize the prior statistical information of the speech. The results show that the proposed method can improve the signal-to-noise ratio up to 0.7 dB under different noises and different signal-to-noise ratios, and the processed speech has better feasibility, which provides good speech enhancement for the processing of noisy speech quality in vocal identification practice without significantly increasing the computational complexity and can be better adapted to the application.

Exact and sampling methods for mining higher-order motifs in large hypergraphs

Network motifs are recurrent, small-scale patterns of interactions observed frequently in a system. They shed light on the interplay between the topology and the dynamics of complex networks across various domains. In this work, we focus on the problem of counting occurrences of small sub-hypergraph patterns in very large hypergraphs, where higher-order interactions connect arbitrary numbers of system units. We show how directly exploiting higher-order structures speeds up the counting process compared to traditional data mining techniques for exact motif discovery. Moreover, with hyperedge sampling, performance is further improved at the cost of small errors in the estimation of motif frequency. We evaluate our method on several real-world datasets describing face-to-face interactions, co-authorship and human communication. We show that our approximated algorithm allows us to extract higher-order motifs faster and on a larger scale, beyond the computational limits of an exact approach.

FusionCalib: Automatic extrinsic parameters calibration based on road plane reconstruction for roadside integrated radar camera fusion sensors

Calibrating the relative positional posture of the roadside sensor with respect to the road is crucial for target tracking and sensor fusion. However, most previous studies rely on prior information or require additional manual measurements, which increases calibration error and labor costs. This paper proposes an automatic extrinsic parameters calibration method for roadside integrated radar camera fusion sensors for the first time. In particular, we leverage the radar camera fusion framework to automatically reconstruct the road plane. Specifically, to integrate sensing abilities, a stable bidirectional selection association method is adopted to match radar and camera trajectories. Based on the association results, the Extended Kalman Filter (EKF) is adopted to fuse pixel positions and three-dimensional (3D) velocities of vehicles, and then a series of 3D positions of vehicle bottoms are obtained to reconstruct the road plane. Experimental results show that our proposed method achieves automatic and accurate calibration of extrinsic parameters.

Wissenschaftsfreiheit, Moralische Kritik und die Kosten des Irrtums

AbstractThis article attempts to do justice to two conflicting positions in current public debates. On the one hand, it defends a strong version of scientific freedom, according to which science should be free, not only from external obstacles and pressures but also from criticism that is based on reasons “of the wrong kind.” The only admissible criterion in debates about scientific claims, I argue, is whether there is sufficient evidence for their truth. Furthermore, I accept the anti-moralistic view that (non-)conformity with moral ideals is never itself evidence. On the other hand, the article nevertheless argues that there is a possibility of legitimate moral criticism of moral claims. This argument relies on the popular (if controversial) idea that there is pragmatic encroachment – specifically, the idea that the standard of sufficiency for evidence is in part determined by pragmatic factors like the costs of error. This opens up the door for a certain type of legitimate moral criticism. For the costs that determine a standard of sufficiency for epistemic justification can also plausibly count as morally significant. There is thus a con-contingent connection between epistemic and moral defects of certain scientific claims. The resulting view is applied to concrete debates, most importantly about race, genetics and IQ.

A robust adaptive-thresholding primal-dual sparse recovery for THz imaging

Terahertz time-domain spectroscopy (THz-TDS) system is a powerful tool in material spectral analysis and non-destructive testing (NDT). However, the data acquisition is time-consuming due to the point-by-point scanning process, which leads to limitations in THz imaging applications. In this paper, a robust adaptive-thresholding primal-dual sparse recovery (APSR) method is proposed to reconstruct high-resolution THz images and reduce the acquisition time. A sparsity averaging dictionary, which is a concatenate of multiple bases, is applied to improve the robustness and generalization. A reweighted scheme is also adopted to enhance the sparse solution. As a crucial consideration in the sparse recovery method, we propose a robust adaptive threshold estimator based on the median absolute deviation in the sense that the final threshold determines the reconstruction quality and the convergence rate. Meanwhile, together with the Nesterov acceleration technique, a large threshold can be applied at the beginning of the iteration to help accelerate the convergence. Numerical experiments demonstrate that the APSR method can reconstruct THz images with robustness even with low SNR (of 30 dB) and low sampling rate (of 30%) compared to conventional methods. In the real THz-TDS system, our proposed sparse imaging method can successfully recover a composite object with much fewer THz spectra and reduces the acquisition time to 30% at the cost of a relative error of 2.5%, demonstrating the efficiency of our proposed method and the penetrating ability of THz technology in the NDT applications.

Day People, Night People: Being Chuck Bosk

Through Forgive and Remember, Charles Bosk made the world of surgical complications and closed‐door morbidity and morbidity conferences visible to a wide audience, helping to remake medical errors from a private into a public issue. In so doing, his work helped to motivate a generation to advocate for higher‐quality, safer health care. But Bosk's introduction to The Price of Perfection: The Cost of Error also highlights how the managerial tools that advocates applied to these problems—“checklists, safety huddles, briefings and debriefings”—invariably missed deeper lessons that Bosk took from his work. Here, a former student and collaborator of Bosk's reflects on Bosk's legacy in relation to the patient safety movement he helped create and the lasting impact of his work on the culture of medical practice.

Summing Up What Can Never be Summed Up: A Meditation on the Work and Legacy of Charles L. Bosk

This special issue of “The Forum” publishes Charles L. Bosk's final work—a book proposal written for a trade audience titled—The Price of Perfection: The Cost of Error. Five noted scholars Carol Heimer, Carla Keirns, Mark Neuman, Julia Szymczak, and Stefan Timmermans offer commentary. Their essays touch on common themes: Bosk's humanity, his wry sense of humor, and keen ability to highlight the absurdity of the social organization of contemporary medicine alongside the sacred obligation that accompanies caring for the sick. Together, the book proposal and these five essays focus our attention on the impact Bosk's scholarship had in medicine, sociology, and the patient safety movement.

Deliver Us from Error: The Perils of Algorithmic Salvation in Medicine

This essay responds to and reflects on the arguments made by Charles Bosk in the introduction to The Price of Perfection: The Cost of Error, written from the perspective of a former sociology doctoral student of his who now works in academic medicine. The author highlights two innovative and persuasive threads running through Bosk's work critiquing the patient safety social movement: (1) the consequence of the social constructedness of medical error and safety solutions, and (2) the importance of the personal, relational, and social dimensions of occupational rituals in clinical practice. The legacy of Bosk's work and impact beyond sociology is considered. The author demonstrates the enduring value of his insights and the impact they can have on current debates about how to transform clinical practice to reduce patient harm.