A Proposal for Parameter-Free Surrogate Building Algorithm Using Artificial Neural Networks

Surrogate models, capable of emulating the robust first principle based models, facilitate the online implementation of computationally expensive industrial process optimization. However, the heuristic estimation of parameters governing the surrogate building often renders them erroneous or under-trained. Current work aims at presenting a novel parameter free surrogate building approach, specifically focusing on Artificial Neural Networks. The proposed algorithm implements Sobol sampling plan and intelligently designs the configuration of network with simultaneous estimation of optimal transfer function and training sample size to prevent overfitting and enabling maximum prediction accuracy. A novel Sample Size Determination algorithm based on a potential concept of hypercube sampling technique adds to the speed of surrogate building algorithm, thereby assuring faster convergence. Surrogates models for a highly nonlinear industrial sintering process constructed using the novel algorithm resulted in 7 times faster optimization.

PELATIHAN PENULISAN DAN PUBLIKASI ARTIKEL ILMIAH GURU-GURU SEKOLAH DASAR (SD) NEGERI GUGUS JOKO TINGKIR SALATIGA

The professional problem that is considered unresolved until now in elementary school teachers is the low productivity of teachers in the field of writing and publishing scientific articles. To participate in solving this problem, community service is held in the Joko Tingkir Cluster Salatiga for three months from July to September 2021. The purpose of this service is to provide training in writing and publishing scientific papers, especially journal articles. Full online implementation model with the In – On method. From the results of the implementation, the majority of participants stated that they had understood how to compile research reports, especially Classroom Action Research (CAR), and had also understood how to write scientific articles and how to publish them in quality scientific journals. Participants have also practiced writing journal articles resulting from CAR, although the percentage who complete the articles is still low. The perceived obstacles are the impasse of ideas at the time of writing, limited references and research time.

Peningkatan Kemampuan Pelaporan dan Analisis Laporan Keuangan pada PT Jaya Persada Indonesia

This Community Service activity is being carried out in collaboration with PT Jaya Perdasa Indonesia (PT JPI), a property development and agency company. Despite the fact that it has been in operation since 2014, partners continue to do traditional financial statement (not yet using an integrated system). Furthermore, financial statement analysis is a critical component that cannot be separated from the business. This is because; financial statement analysis is used to describe a company's health as well as a foundation for making strategic business decisions. The implementation of Community Service activities is divided into three stages: preparation, online implementation, and activity evaluation. This training increases participants' understanding and knowledge of the importance of producing good financial reports using an integrated system, resulting in more comprehensive financial reports. Furthermore, this training improves participants' skills and expertise in reporting, analyzing, and interpreting the results of the company's financial statement analysis. Abstrak Kegiatan Pengabdian Masyarat ini bermitra dengan PT Jaya Perdasa Indonesia (PT JPI) yang bergerak dalam bidang pengembangan dan keagenan properti. Walapun sudah beroperasi sejak tahun 2014, mitra masih melakukan pembukuan laporan keuangan dengan cara yang konvensional (belum menggunakan suatu sistem yang terintegrasi). Selanjutnya analisis laporan keuangan merupakan elemen penting yang tidak bisa dipisahkan dari perusahaan. Hal ini disebabkan karena selain digunakan untuk mengetahui gambaran terhadap sehat-tidaknya suatu perusahaan, analisis laporan keuangan juga menjadi salah satu dasar untuk mengambil keputusan strategis pada bisnis. Pelaksanan kegiatan Pengabdian Masyarat dilakukan melalui tiga tahap yang meliputi tahap persiapan, pelaksanaan secara daring, dan tahap evaluasi kegiatan. Pelatihan ini menambah wawasan dan pengetahuan peserta terhadap pentingnya membuat laporan keuangan yang baik dengan suatu sistem yang sudah terintegrasi, sehingga laporan keuangan yang dibuat menjadi lebih komprehensif. Selain itu, pelatihan ini juga menambah keterampilan dan keahlian peserta dalam pelaporan, menganalisis dan menginterprestasi hasil analisis laporan keuangan perusahaan.

Internet-based language production research with overt articulation: Proof of concept, challenges, and practical advice

Language production experiments with overt articulation have thus far only scarcely been conducted online, mostly due to technical difficulties related to measuring voice onset latencies. Especially the poor audiovisual synchrony in web experiments (Bridges et al. 2020) is a challenge to time-locking stimuli and participants’ spoken responses. We tested the viability of conducting language production experiments with overt articulation in online settings using the picture–word interference paradigm – a classic task in language production research. In three pre-registered experiments (N = 48 each), participants named object pictures while ignoring visually superimposed distractor words. We implemented a custom voice recording option in two different web experiment builders and recorded naming responses in audio files. From these stimulus-locked audio files, we extracted voice onset latencies offline. In a control task, participants classified the last letter of a picture name as a vowel or consonant via button-press, a task that shows comparable semantic interference effects. We expected slower responses when picture and distractor word were semantically related compared to unrelated, independently of task. This semantic interference effect is robust, but relatively small. It should therefore crucially depend on precise timing. We replicated this effect in an online setting, both for button-press and overt naming responses, providing a proof of concept that naming latency – a key dependent variable in language production research – can be reliably measured in online experiments. We discuss challenges for online language production research and suggestions of how to overcome them. The scripts for the online implementation are made available.

Ensemble Learning Models for Food Safety Risk Prediction

Ensemble learning was adopted to design risk prediction models with the aim of improving border inspection methods for food imported into Taiwan. Specifically, we constructed a set of prediction models to enhance the hit rate of non-conforming products, thus strengthening the border control of food products to safeguard public health. Using five algorithms, we developed models to provide recommendations for the risk assessment of each imported food batch. The models were evaluated by constructing a confusion matrix to calculate predictive performance indicators, including the positive prediction value (PPV), recall, harmonic mean of PPV and recall (F1 score), and area under the curve. Our results showed that ensemble learning achieved better and more stable prediction results than any single algorithm. When the results of comparable data periods were examined, the non-conformity hit rate was found to increase significantly after online implementation of the ensemble learning models, indicating that ensemble learning was effective at risk prediction. In addition to enhancing the inspection hit rate of non-conforming food, the results of this study can serve as a reference for the improvement of existing random inspection methods, thus strengthening capabilities in food risk management.

SmartScan: An Intelligent Online Scan Sequence Optimization Approach for Uniform Thermal Distribution, Reduced Residual Stresses and Deformations in PBF Additive Manufacturing

Parts produced by laser or electron-beam powder bed fusion (PBF) additive manufacturing are prone to residual stresses, deformations, and other defects linked to non-uniform temperature distribution during the manufacturing process. Several researchers have highlighted the important role scan sequence plays in achieving uniform temperature distribution in PBF. However, scan sequence continues to be determined offline based on trial-and-error or heuristics, which are neither optimal nor generalizable. To address these weaknesses, we have articulated a vision for an intelligent online scan sequence optimization approach to achieve uniform temperature distribution, hence reduced residual stresses and deformations, in PBF using physics-based and data-driven thermal models. This paper proposes SmartScan, our first attempt towards achieving our vision using a simplified physics-based thermal model. The conduction and convection dynamics of a single layer of the PBF process are modeled using the finite difference method and radial basis functions. Using the model, the next best feature (e.g., stripe or island) that minimizes a thermal uniformity metric is found using control theory. Simulations and experiments involving laser marking of a stainless steel plate are used to demonstrate the effectiveness of SmartScan in comparison to existing heuristic scan sequences for stripe and island scan patterns. In experiments, SmartScan yields up to 43% improvement in average thermal uniformity and 47% reduction in deformations (i.e., warpage) compared to existing heuristic approaches. It is also shown to be robust, and computationally efficient enough for online implementation.

The Influence of Online Learning on the Development of Students at SD Muhammadiyah 5 Porong During the Covid Pandemic

The COVID-19 pandemic has disrupted the online learning process. So a solution is needed to answer these problems. Online learning can solve this problem. The purpose of this study was to obtain an overview of online implementation at Muhammadiyah 5 Porong Elementary School. The research method used the interview method by asking questions to 1 respondent. Based on the data it was found that all students did online learning and it was carried out according to their respective lesson schedules. The material information obtained through online learning is not effective for elementary school age children. The media used during online learning and the majority use the Google Classroom and Google Meet systems. Although this system can be used as a solution for conditions during the COVID-19 pandemic, several obstacles such as unstable internet networks, limited quotas, and others. In conditions of an outbreak Covid-19, online learning can be used with consideration of students' conditions, so they will get used to adjusting to the online system, learning can be done well. In addition, this online system can be used additional experiences for students in the future.The positive influence can be done anywhere and anytime according to the existing conditions. Because more time is spent at home, online learning can also increase the closeness between students and their parents. The negative influence of students who complain about the ineffectiveness of online learning, such as too many assignments, different internet networks for each student, difficulty understanding the material provided by the teacher, students also get bored quickly, and many others.

Fresh new look for system-level prognostics

Model-based prognostic approaches use first-principle or regression models to estimate and predict the system’s health state in order to determine the remaining useful life (RUL). Then, in order to handle the prediction results uncertainty, the Bayesian framework is usually used, in which the prior estimates are updated by infield measurements without changing the model parameters. Nevertheless, in the case of system-level prognostic, the mere updating of the prior estimates, based on a predetermined model, is no longer sufficient. This is due to the mutual interactions between components that increase the system modeling uncertainties and may lead to an inaccurate prediction of the system RUL (SRUL). Therefore, this paper proposes a new methodology for online joint uncertainty quantification and model estimation based on particle filtering (PF) and gradient descent (GD). In detail, the inoperability input-output model (IIM) is used to characterize system degradations considering interactions between components and effects of the mission profile; and then the inoperability of system components is estimated in a probabilistic manner using PF. In the case of consecutive discrepancy between the prior and posterior estimates of the system health state, GD is used to correct and to adapt the IIM parameters. To illustrate the effectiveness of the proposed methodology and its suitability for an online implementation, the Tennessee Eastman Process is investigated as a case study.

Development of a Framework for Activation of Aggregator Led Flexibility

This paper presents a novel framework architecture for an online, real-time flexibility assessment and activation platform targeted at unlocking the flexibility potential of commercial buildings and smaller industrial sites, thereby enabling greater levels of renewable grid integration. Renewable integration targets in Europe of up to 40% of power generation from renewable sources by 2030 and over 90% by 2050 aim to decarbonize the electrical grid and increase electrification of transport, industry, and buildings. As renewable integration targets increase, participation in flexibility programs will be required from a much greater range of buildings and sites to balance grids hosting high levels of renewable generation. In this paper, an online implementation of a standardized flexibility assessment methodology, previously developed for offline contract negotiations between stakeholders, is modified to automate the assessment. The automated assessment is then linked to an aggregator-based multi-building or site optimization stage, enabling increased participation of multiple buildings and sites. To implement the assessment, models for individual flexible systems were reviewed, selected, and adapted, including physics-based, data-driven, and grey-box models. A review of optimization for flexibility found mixed-integer linear programming to be the optimal approach for the selection of flexible systems for demand response events.