Performance Criteria Research Articles

Mechanical Analysis and Optimization of Concrete Structures Based on Advanced Finite Element Method

This article explores the mechanical analysis and optimization problems of concrete structures based on the Advanced Finite Element Method. By integrating advanced numerical techniques with practical engineering cases, this study aims to improve the safety and economy of concrete structure design. Firstly, the paper outlines the limitations of traditional finite element methods in concrete structure analysis, such as insufficient computational accuracy and low computational efficiency. Subsequently, by introducing AFEM, we significantly improved the accuracy and efficiency of the analysis. For example, when simulating a complex bridge structure, AFEM not only reduces the calculation time by about 25%, but also improves the accuracy of stress distribution prediction by more than 10%. In the optimization stage, we utilized the analysis results of AFEM and optimized the material consumption, cross-sectional dimensions, and reinforcement parameters of concrete structures through multi-objective optimization algorithms. A comprehensive data analysis underscores that the optimized concrete structure triumphantly meets all safety performance criteria while achieving a remarkable 12% reduction in material usage. This substantial material savings translates into a substantial 8% decrease in overall construction costs, significantly bolstering the project’s economic feasibility. Moreover, these cost savings not only amplify the project’s profitability but also play a pivotal role in enhancing the structure’s longevity and durability, thereby contributing to its sustainable performance over its entire service life. Furthermore, we delved into the capability of AFEM in simulating intricate phenomena such as the nonlinear behavior of concrete materials, crack propagation patterns, and the intricate interactions between steel reinforcements and concrete. These complex mechanical behaviors are crucial for the safety and stability of structures, and AFEM provides more comprehensive and accurate references for structural design by accurately simulating these behaviors. This article conducts in-depth research on the mechanical analysis and optimization of concrete structures based on AFEM, and demonstrates the significant advantages of AFEM in improving structural safety and economy through specific data. These research results not only provide new theoretical support and practical tools for concrete structure design, but also provide valuable references for future research and application in related fields.

Distributed Optimization Control for the System with Second-Order Dynamic

No matter whether with constraint or without constraint, most of the research about distributed optimization is studied for the kind of quadratic performance criteria that does not have an integrator; these optimization problems only concern the state value at the time of the final state, not the whole process of the system change. For this problem, this paper discusses second-order multi-agent systems with a discrete-time dynamic and a continuous-time dynamic, respectively, for distributed optimization control problems, and proposes sufficient conditions to ensure the quadratic performance criteria with an integrator is positive. Specifically, under sufficient conditions, we describe the multi-agent systems that are considered in this paper to be connected topology; all the agents can obtain the information from their neighbors. In addition, the structure of our controller only relies on the Laplace matrix of the system’s topology, and the reaction coefficients in the controller are the parameters in the performance criteria. Finally, the analysis of convergence is given and verified by numerical examples and simulations.

A Framework for Prioritising the Performance Criteria of Natural Fibre Composite Materials: Incorporation of CRITIC-TOPSIS Method

Selecting an appropriate Multi-Criteria Decision-Making (MCDM) method to provide a solution to assist design engineers in prioritising the right criteria in the early design process is essential. Part of the aim of this study is to establish an integration CRITIC-TOPIS for selecting the most efficient framework to choose performance criteria, namely density, tensile strength, Young’s modulus, cellulose, and elongation at break for natural fibre material intended for cap toe shoes like abaca, bamboo, coir, jute, kenaf, sisal. Hence, a new framework was proposed and tested based on integrating Criteria Importance Through Inter Criteria Correlation (CRITIC)-Technique Order Preference by Similarity to Ideal Solution (TOPSIS). Therefore, this proposed framework consists of two phases: the first involves determining the weights of attributes using the CRITIC method, and the second consists of making material criteria decisions using the TOPSIS method. Meanwhile, to achieve this objective, numerical validation was obtained using data from selected past case studies, which were then replicated to validate the output of the proposed framework. According to the validation conducted using CRITIC-TOPSIS, the results show a significant level of similarity, with the rankings being consistent. Therefore, the proposed methodology may provide imprecise and ambiguous information for prioritising the performance criteria of natural fibre composite materials. Moreover, design engineers can utilise this framework in the composite industry to create the best possible evaluation model for composite material criteria selection for various applications.

On the Design of a Simulation-Assisted Human-Centered Quasi-Stiffness-Based Actuator for Ankle Orthosis

Most exoskeletons designed to assist users in load-bearing tasks face a mechanical dilemma in their conception. Designers may find a compromise between stiff active actuators-based architectures which are powerful but bulky and compliant actuator-based designs which are much less assistive but less constraining for users. This article presents a new open-source simulation-based design tool and a human-centered method that lets orthosis designers explore different device configurations and evaluate some performance criteria. This framework was applied in three different young-adult subjects. The effects of design personalization on user morphology and gait were studied. First, an ankle–foot orthosis designed to support a 20 kg backpack was defined according to the user’s height, weight, and walking speed. Then, a simulation of the subjects fitted with their customized design walking at a self-selected speed on flat ground carrying this additional load was performed. First, the results showed that the designed method inspired by natural joint stiffness behavior provided viable personalized mechanisms. Second, significant reductions in peak joint torque and mean joint activity were observed when comparing muscle-generated torques while the subject was wearing the 20 kg backpack with ankle–foot orthoses on both legs or without. Finally, it shows the value of an open-access tool for exploring the coupling of passive and active actuators to generate lighter and more compliant designs.

Learning to rank anomalies: scalar performance criteria and maximization of rank statistics

AbstractThe ability to collect and store ever more massive data, unlabeled in many cases, has been accompanied by the need to process them efficiently in order to extract relevant information and possibly design solutions based on the latter. In various situations, the vast majority of the observations exhibit the same behavior, while a small proportion deviates from it. Detecting these outlier observations (or equivalently defined as anomalies) is now one of the major challenges for machine learning applications (e.g. fraud detection or predictive maintenance). We propose here a novel methodology for outlier/anomaly detection, by learning a scoring function defined on the feature space allowing for ranking the observations by degree of abnormality. The scoring function is built through maximization of an empirical performance criterion taking the form of a (two-sample) linear rank statistic. We show that bipartite ranking algorithms can thus be used to learn nearly optimal scoring function with provable theoretical guarantees. We illustrate our methodology with numerical experiments based on open access online code.

Brain and eye movement dynamics track the transition from learning to memory-guided action

Learning never stops. As we navigate life, we continuously acquire and update knowledge to optimize memory-guided action, with a gradual shift from the former to the latter as we master our environment. How are these learning dynamics expressed in the brain and in behavioral patterns? Here, we devised a spatiotemporal image learning task ("Memory Arena") in which participants learn a set of 50 items to criterion across repeated exposure blocks. Critically, brief task-free periods between successive image presentations allowed us to assess multivariate electroencephalogram (EEG) patterns representing the previous and/or upcoming image identity, as well as anticipatory eye movements toward the upcoming image location. As expected, participants eventually met the performance criterion, albeit with different learning rates. During task-free periods, we were able to readily decode representations of both previous and upcoming image identities. Importantly though, decoding strength followed opposing slopes for previous vs. upcoming images across time, with a gradual decline of evidence for the previous image and a gradual increase of evidence for the upcoming image. Moreover, the ratio of upcoming vs. previous image evidence directly followed behavioral learning rates. Finally, eye movement data revealed that participants increasingly used the task-free period to anticipate upcoming image locations, with target-precision slopes paralleling both behavioral performance measures as well as EEG decodability of the upcoming image across time. Together, these results unveil the neural and behavioral dynamics underlying the gradual transition from learning to memory-guided action.

Classification of Faults in Power System Transmission Lines Using Deep Learning Methods with Real, Synthetic, and Public Datasets

Every part of society relies on energy systems due to the growing population and the constant demand for energy. Because of the high energy demands of transportation, industry, and daily life, energy systems are crucial to every part of society. Electrical transmission lines are a crucial component of the electrical power system. Therefore, in order to determine the power system’s protection plan and increase its reliability, it is critical to foresee and classify fault types. With this motivation, the main goal of this paper is to design a deep network model to classify faults in transmission lines based on real, generated, and publicly available datasets. A deep learning architecture that was based on a one-dimensional convolutional neural network (CNN) was utilized in this study. Accuracy, specificity, recall, precision, F1 score, ROC curves, and AUC were employed as performance criteria for the suggested model. Not only synthetic but also real data were used in this study. It has been seen that the created model can be used successfully for both real data and synthetic data. In order to measure the robustness of the network, it was tested with three different datasets consisting of real, generated, and publicly available datasets. In the paper, 1D CNN, one of the machine learning methods, was used on three different power systems, and it was observed that the machine learning method was successful in all three power systems.

A Review of the Lawrence Livermore Nuclear Accident Dosimeter 1980s-present

A Nuclear Accident Dosimetry program is a federal requirement for all facilities that have the potential to have a criticality accident. Personnel Nuclear Accident Dosimeter (PNAD) theory and analytical procedures are driven by various scientific needs and interacting regulations. A brief history of the status of USA Department of Energy (DOE) nuclear accident dosimetry regulations, recommendations, and performance testing criteria are given. Then, the history of the Lawrence Livermore National Laboratory (LLNL) PNAD is explored, including changes in the physical dosimeter and adjustments of the analysis method through the last four decades. Finally, the performance of LLNL’s PNAD at criticality accident intercomparison training exercises since 2009 is explored. In general, reported neutron doses have been within or close to DOE-STD-1098 performance criteria while reported gamma doses have been outside of DOE-STD-1098 performance criteria. Reported total absorbed doses have varied in meeting ANSI/HPS N13.3 and ANSI/HPS N13.3 (R2019) performance criteria. Dosimetry staff retirement and turnover have left historical knowledge gaps, yet provided opportunities within the NAD program at LLNL. This review paper serves as an overview of the history and status of the NAD program. Brief technical, procedural and programmatic recommendations to improve LLNL’s NAD program are given. Technical recommendations include investigating orientation factors through modeling or empirical experimentation, investigating gamma dosimetry methods for high-dose scenarios, and exploring other dosimetric methods for simpler, quicker NAD analysis. Procedural recommendations include better documentation of conversion factor (activity-to-fluence and fluence-to-dose) derivations and spectrum uses, and updated analysis spreadsheets or simple Graphic User Interfaces for dose calculations. Programmatic recommendations include formalized training for NAD analysts, and having multiple SMEs trained on the NAD program.

Optimizing Supply Chain Transparency and Customer Compatibility with AI-Driven Models

As global supply chains become increasingly complex, the adoption of artificial intelligence (AI) technologies has emerged as a critical strategy for enhancing operational transparency and improving customer compatibility. This study investigates the application of AI-driven models in optimizing supply chain performance, focusing on predictive analytics, real-time data integration, and customer-centric personalization. A comprehensive experimental framework was employed, evaluating five distinct AI configurations against four key performance criteria: operational transparency, customer compatibility, cost efficiency, and delivery performance. Results demonstrated that the Real-Time Data Integration Model achieved a 20% improvement in operational transparency, allowing for enhanced visibility into inventory management and more agile responses to dynamic demand fluctuations. Additionally, the Customer-Centric Personalization Model increased customer satisfaction by 10%, emphasizing the critical role of tailored service delivery in modern supply chain management. The Cost Optimization Model yielded significant cost reductions, improving cost efficiency by 18%, though it showed a marginal decrease in customer compatibility. The findings highlight the trade-offs between cost efficiency and customer-centric strategies, suggesting that a balance is required to achieve a well-rounded and sustainable supply chain model. This research underscores the transformative potential of AI in driving efficiency, transparency, and customer satisfaction. Future work should explore the integration of advanced technologies such as 5G and further investigate scalable AI solutions capable of addressing the evolving challenges faced by global supply chains.

Artificial Intelligence: The Doers, the Don’t-ers, and the continuum between

ABSTRACT Session 1: Make My Syllabus Nicer: Exploring Generative AI for Inclusivity and Accessibility in LIS Instruction Rebecca J.Morris (University, of Pittsburgh, School of Computing and Information, US) Though certainly not a magic formula, LIS instructors’ use of generative AI tools can support accessibility and inclusivity in design and adaptation of course materials and communications. LIS faculty may likely know and implement accommodations such as Universal Design for Learning to some extent, but perhaps find it challenging to integrate methods more thoroughly for reasons such as time, number of courses or varied formats of courses, and number of disability accommodations. This session includes teaching tasks: making tone and content of syllabi and assignment instructions more transparent and student-friendly generating performance criteria, checklists, and rubrics for differentiated assignments integrating principles of Universal Design for Learning into course design elements, such as o creating text outlines of lectures to provide multiple means of representation o personalizing assessment options to offer multiple means of action and expression A Community Conversation: The Context, Complexities, Contradictions and Critical Concerns of Artificial Intelligence: Parts I & II This dual presentation offers perspectives on the complexities, contradictions, and critical issues concerning Artificial Intelligence (AI). The IP SIG combines presentations with interactive audience engagement, providing an opportunity for ALISE members and attendees to forge cross-disciplinary ties around the conference themes of “[t]he ways in which information is created, disseminated, and altered” in the current education landscape Session 2: Part 1- The Context, Considerations and Complexities Rhiannon Bettivia (Simmons University School of Library and Information Science, US) Co-Moderator Warren Crichlow (Emerita, York University, US) Anthony W. Dunbar, (Dominican University - School of Information Studies, US) Co-Moderator Theodora Kourkoulou (University of Illinois at Urbana-Champaign, US) Cameron McCarthy (Emerita, University of Illinois at Urbana-Champaign, US) The panel centers innovation as a necessity and change agent in the area of digital and technological literacy. Thus, offering a brief historical discussion of technological developments and their deployment in classrooms, grounding a discussion of AI. Then discusses the dichotomy that AI is either the ultimate positive technological contribution to society or it will move society from a state of techno determinism to techno fatalism. This discussion centers the nuances between the pollyannic and fatalist propositions. Consisting of submissions from LIS educators and practitioners eager to share their experiences using (or not) AI in the classroom, this conversation further shapes approaches to AI and contributes to developing policies and practices at institutional and disciplinary levels. Session 3: Panel 2 - The Contradictions and Critical Concerns Rhiannon Bettivia (Simmons University School of Library and Information Science, US) Moderator Warren Crichlow (Emerita, York University, US) Panelist Theodora Kourkoulou (University of Illinois at Urbana-Champaign, US) Panelist Cameron McCarthy (Emerita, University of Illinois at Urbana-Champaign, US) Panelist Part 2 dives deeper into the pedagogical Contradictions and related Critical Concerns that LIS scholars, instructors, and practitioners are grappling with as AI interrupts and, in many instances, disrupts LIS learning spaces. This session offers an interdisciplinary sensibility.

Machine learning based prediction for bulk porosity and static elastic modulus of Yungang Grottoes sandstone

In this work, four mainstream machine learning (ML) techniques are used to evaluate the bulk porosity and static elastic modulus of weathered Yungang Grottoes sandstone. Datasets are gathered from the experiments, which includes 432 groups effective experimental data including 8 inputs features. bulk porosity and static elastic modulus were considered as outputs to determine the weathering degrees of Yungang Grottoes sandstone. The 4 performance criteria were used to evaluate the ML models. Results demonstrate that the Artificial Neural Network (ANN) is the best-fitted models for estimating the bulk porosity and static elastic modulus compared to Multiple Linear Regression (MLR), Support Vector Regression (SVR), Gaussian Process Regression (GPR). The accuracy of the trained model for static elastic modulus is slightly higher than that of bulk porosity. The GPR and ANN model can accurately predict the bulk porosity and static elastic modulus in training stages. The ANN with multi-hidden layers developed is competent with high degree of precision and generalization ability for bulk porosity and static elastic modulus compared to other selected regression-based ML models (MLR, SVR, and GPR). The coefficient of determinations of ANN in the range of (0.9537–0.9641) during the testing stages is more stable and higher than that of (0.8883–0.9453) other built ML models. The prediction efficiency of pretrained ANN model was well adjusted for the actual and forecast datasets at the training and testing stages, and the error range was no more than 0.7% and 0.15 GPa at both stages of prediction for bulk porosity and static elastic modulus respectively. And the ANN based static elastic modulus prediction model’s error proportions significantly decreased and were confined to a modest range between + 10% and − 10%. The proposed surrogate models are valid for the bulk porosity ranging from 7 to 14% and the static elastic modulus ranging from 0.7 to 1.4 Gpa, which can be utilized for the accurate and fast prediction of the weathering degrees of Yungang Grottoes sandstone.

Optimizing marine vehicles industry: a hybrid analytical hierarchy process and additive ratio assessment approach for evaluating and selecting IoT-based marine vehicles

Rapid developments in the Internet of Things (IoT) have opened the door for game-changing applications in numerous sectors, especially the vehicle industry. There is a rising demand for efficient assessment and decision-making methodologies to pinpoint the most promising choices for the vehicle sector with the introduction of IoT-based maritime vehicles. To overcome this issue, the integrated multi-criteria decision-making analysis (MCDA) paradigm proposed in this research combines the additive ratio assessment (ARAS) and analytic hierarchy process (AHP) approaches to evaluate and choose IoT-based maritime vehicles based on their performance- and authenticity-related criteria in the vehicle sector. The selection issue is hierarchically organized, and the assessment criteria are prioritized using the AHP approach. There are seven performance and authentication related criteria are selected that might aid in the selection procedure. Using the AHP, we are assigned these criteria proportionate weights that reflect their respective significance and interrelationships. AHP, however, falls short of offering a thorough analysis of the alternatives that exist. To overcome these restrictions, this research presents the integration of AHP with the ARAS approach for the ranking of alternatives according to how well they perform against the set criteria. By using the ARAS technique, it is possible to get over the restrictions of AHP and achieve a more thorough assessment of maritime IoT-based vehicles. The efficiency of the framework is proven using empirical data and professional judgment. The findings show that the hybrid method successfully encapsulates the intricate relationships between the factors being evaluated and objectively appraises the potential of IoT-based maritime vehicles for the automotive sector. This study extends to the area by providing an organized and thorough method for assessing and choosing IoT-based maritime vehicles. Considering several factors and their mutual dependence, the hybrid AHP and ARAS technique gives decision-makers a powerful tool for evaluating the potential of IoT-based maritime vehicles in the automotive sector. Smart decisions on the deployment of IoT-based marine vehicles and maximizing the potential they present may be made by beneficiaries in the automotive sector using the study’s results.

Development of an exposure assessment industrial model for Bacillus cereus in rice matrix containing insect chitosan.

An exposure assessment model for industrial use has been developed by using kinetic data from inactivation and growth of Bacillus cereus spores. It can provide a valuable tool for estimating the concentration of B. cereus after a storage period of 24 h at a specified temperature (20 °C) and for an estimation of the percentage of contaminated portions according to the input data of the model. This model considers a rice-derived product that has undergone a standard cooking process at 95 °C for 20 min. According to the results, the presence of chitosan affects the final microbial load after storage, potentially serving as an additional control measure in the event of cold chain abuse or break. Chitosan's antimicrobial properties likely play a role in reducing microbial growth during storage, thereby contributing to enhanced food safety. In practical terms, this suggests that incorporating chitosan into food products, especially those susceptible to microbial contamination like rice derivatives, could help mitigate risks associated with temperature abuse or cold chain disruptions. By acting as a protective barrier against microbial proliferation, chitosan offers a preventive measure to maintain product quality and safety throughout the supply chain. Considering two scenarios, 104 or 107 as initial contamination the model estimated that the 55 and 100% of portions would be respectively contaminated, according to a Performance Criteria of 4 log reductions.

An investigation of machine learning algorithms for prediction of temporomandibular disorders by using clinical parameters.

This study aimed to predict temporomandibular disorder (TMD) using machine learning (ML) approaches based on measurement parameters that are practically acquired in clinical settings. 125 patients with TMD and 103 individuals without TMD were included in the study. Pain intensity (with visual analog scale), maximum mouth opening (MMO) and lateral excursion movements (with millimeter ruler), cervical range of motion (with goniometer), pressure pain threshold (PPT; with algometer), oral parafunctional behaviors (with Oral Behaviors Checklist), psychological status (with Hospital Anxiety and Depression Scale), and quality of life (with Oral Health Impact Profile) were evaluated. The measurements were analyzed via over 20 ML algorithms, taking into account an extensive parameter tuning and cross-validation process. Results of variable importance were also provided. Bagging algorithm using Multivariate Adaptive Regression Spline (MARS) algorithm (accuracy = 0.8966, area under receiver operating characteristic curve = 0.9387, F1-score = 0.9032) was the best performing model regarding the performance criteria. According to this model, the 5 most important variables for predicting TMD were pain intensity, MMO, lateral excursion and PPT values of masseter and temporalis anterior muscles, respectively. The Bagging algorithm using the MARS algorithm is a robust model that, in combination with clinical parameters, assists in the detection of patients with TMD in settings with limited capabilities. The clinical parameters and ML algorithm proposed in this study may assist clinicians inexperienced in TMD to make a preliminary detection of TMD in clinics where diagnostic imaging tools are limited.

Fire performance of CFRP-strengthened piloti-type columns with fire-resistant materials during standard fire exposure

This paper presents a numerical investigation into the fire endurance of carbon fiber reinforced polymer (CFRP)-strengthened columns, shielded with fire-resistant materials, in piloti-type reinforced concrete buildings. The strengthened column, equipped with a fire protection system, underwent exposure to the ASTM E119 standard time-temperature curve for a duration of 4 h. To comprehensively evaluate the thermal and structural performance of the strengthened column at elevated temperatures and substantiate the effectiveness of the fire protection system, a fully coupled thermal-stress analysis was conducted. The numerical modeling approach employed in this study was rigorously validated through previous experimental studies in conjunction with adherence to the ACI design guideline, specifically ACI 440.2R-17. Using the validated structural fire model, the thermal and structural behaviors of the RC column with an insulated CFRP strengthening system were investigated based on four key performance criteria: glass transition temperature, ignition temperature of polymer matrix, critical temperature of reinforcing bars, and the design axial load capacity at elevated temperatures. Furthermore, a comparative assessment of fire endurance was performed using diverse fire-resistant materials, including Sprayed Fire-Resistive Material (SFRM) and Sikacrete®-213 F, with insulation thicknesses ranging from 10 to 30 mm, during the 4-hour fire exposure period.

Investigation of Performance Specifications for Marshall Mixes Using Performance-Based Mix Design Methodology

This paper presents a comprehensive study for developing the performance specifications for the Marshall mixes using the performance-based mix design (PMD) methodology. The specification describes the desired levels of fundamental engineering properties for predicting performance during mix design or quality assurance checks at the time of construction. In this study, the bituminous concrete mixture was designed through complementing volumetric criteria with performance tests such as dynamic creep and the indirect tensile asphalt cracking test for evaluating resistance against rutting and fatigue cracking, respectively. This study considers different factors such as five different types of design aggregate gradations (mid-gradation, fine-coarse gradation, coarse gradation, coarse-fine gradation, and fine gradation), three binder types (VG-30, VG-40, and PMB-40), and five levels of compactive efforts (35, 50, 75, 90, and 110 blows on each face). The study finds the effect of design aggregate gradation, binder type, and compactive effort on the rutting and cracking resistance of bituminous mixes. The research study also comprehends the influence of volumetric properties on the performance parameters. Further, it establishes the initial performance criteria for rutting and cracking for Marshall mixes. The performance space diagram (PSD) has also been plotted, portraying the overall performance (acceptable, desirable, or unsatisfactory performance regions) of bituminous mixtures based on rutting resistance and cracking resistance. This paper highlights and advocates the integration of these performance-based specifications into the Marshall mix design specifications for improving pavement characteristics and extending overall service life.

An integrated simulation-data envelopment analysis approach for impact of line-seru conversion

This study aims to design an innovative method to evaluate the effects of line-seru conversion in terms of various environmental and economic performance criteria. For this purpose, an integrated approach using simulation and data envelopment analysis (DEA) assisted with various performance criteria is proposed for the first time. The simulation studies involve diverse scenarios for the production of a circuit breaker on assembly lines balanced with RPW and COMSOAL heuristic algorithms, and seru production systems. The simulation results indicate a significant increase in labour effectiveness and station/yatai utilization ratios, complete removal of WIP inventories, which led to a growth in productivity up to 43.29%. The number of workers, carbon-dioxide emission, required work-space, training costs of workers and equipment cost data were incorporated in the results to evaluate the applicability and relative efficiency of the developed scenarios by use of the CCR and BCC models of DEA. The calculated scale efficiency shows that the scenarios for assembly lines balanced with COMSOAL and the seru-based scenarios were both fully effective. The seru production system scenario with 6 yatais was the most effective scenario due to reduced task times after conversion. Also, line-seru conversion proved to be advantageous despite the high training costs.

A hosting capacity based approach toward distribution system planning for high PV penetration

This paper presents an approach to estimate the hosting capacity for distribution networks considering the impact of PV penetration at different voltage levels. The estimation and the method were selected such that the results were most suitable for distribution system planning. A time-series based method was used as it covers significant aspects needed for prioritising network reinforcement. The MV background voltage was modelled varying in time, assuming the same penetration level in the other LV networks supplied by the same MV system. The hosting capacity is defined as the maximum acceptable PV size per customer for a given PV penetration. Based on the different possible combinations of PV location, the probability of overvoltage and overloading is used as a performance index. The planning risk is used as a limit for the performance criterion. The method can be automated for a large number of networks due to using an IEC 61970-based input format. It also enables linking DSO network models to customer smart metre databases. The severity and risks of limit violations are analysed with different metrics from the time-series simulations. The change in background voltage with increasing penetration is shown to impact the results significantly. When considering it, the estimated hosting capacity was reduced by 32 %, on average.

Evaluating Annual Sun Exposure in LEED v4 for Commercial Office Buildings: Inclusion of Annual Glare to Enhance the Occupant Visual Performance and Comfort

ABSTRACT LEED is one of the most widely adopted building benchmarks globally, and it encompasses multiple rating criteria for performance. However, such criteria typically focus on reducing energy consumption and may disregard providing a high level of occupant satisfaction. LEED focuses on a desk height evaluation of Spatial Daylight Autonomy and Annual Sunlight Exposure to predict the daylight availability and probability of glare, respectively, which may disregard an occupant’s visual comfort at eye level. This paper proposes a more comprehensive approach to daylighting performance evaluation in a commercial office space that incorporates Annual Glare – vertical-eye level glare evaluation, in addition to Spatial Daylight Autonomy and Annual Sunlight Exposure. A comparison between Annual Sunlight Exposure and Annual Glare is established to understand the criteria missing in LEED that evaluates discomfort glare and improves the visual performance and comfort of the occupants significantly.

Automatic shock excitation system for dynamic calibration of six-axis force/torque sensor

The coupling behavior between the six-axis force/torque (F/T) sensor and the mechanical environment introduces variations in the dynamic parameters obtained through different calibration methods and devices, which poses challenges in proposing dynamic performance criteria. This paper presents a comprehensive automatic calibration system for determining the dynamic characteristics of the six-axis F/T sensor. Impulse force generators (IFGs), positioned at various orientations, apply three mutually orthogonal directional force and torque components to the sensor using a cruciform artifact. The system ensures repeatable impulse excitation by maintaining consistent spring compression in the IFG, measured by the displacement control system's grating ruler. The pulse width and frequency response function (FRF) are determined based on the response signal without measuring the input signal. The calibration frequency and the impact force magnitude can be controlled by selecting appropriate system parameters. Compared to manual impulse hammers, IFGs exhibit superior features such as high bandwidth, repeatability, and controllable force amplitude. They provide high-quality impulse excitation with calibration frequencies and forces up to 30 kHz and 2500 N or even higher, respectively. Furthermore, a linear relationship between peak value of impulse excitation and spring compression is studied to ensure effective excitation application. The consistency of dynamic calibration results of the six-axis F/T sensor verifies the effectiveness of this automatic calibration system.