Article
Engineering, Environmental
Jing Wu, Morten Lind, Xinxin Zhang, Karnati Pardhasaradhi, Sharat Kumah Pathi, Claus Marner Myllerup
Summary: This paper introduces the development trend of introducing artificial intelligence tools in complex industrial systems in the industry 4.0 environment, with a focus on the Multilevel Flow Modelling (MFM) method in knowledge-based operation support systems. A procedure for knowledge acquisition and representation to improve model quality is proposed, along with the introduction of a new reasoning engine for real-time cause-consequence reasoning and methods for model verification, validation, and performance evaluation. Case studies demonstrate the effectiveness of intelligent operation support by applying MFM to an off-shore water injection system.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2021)
Article
Green & Sustainable Science & Technology
Ivan Litvaj, Olga Ponisciakova, Dana Stancekova, Jaroslava Svobodova, Jozef Mrazik
Summary: This paper discusses the complexity of decision-making faced by managers in a dynamic and turbulent environment, particularly in relation to quality management. It emphasizes the importance of the connection between theory and practice, and highlights the use of procedures, methods, and knowledge in the decision-making process within the context of quality management.
Article
Public, Environmental & Occupational Health
Jeffrey M. Keisler, Igor Linkov
Summary: Recent guidelines for risk-informed decision making provide a standard for incorporating probabilistic risk models with other considerations, but quantifying risk is difficult when threats, vulnerabilities, and consequences are highly uncertain. Decision making informed by risk (DMIR) can be used as a flexible approach that combines risk and decision analytics. Multi-criteria decision analysis (MCDA) is commonly used as a basis for DMIR to accommodate varying levels of analytical detail.
Article
Computer Science, Interdisciplinary Applications
Foivos Psarommatis, Dimitris Kiritsis
Summary: This study focuses on detection and repair-based Zero Defect Manufacturing (ZDM) strategies and develops a Decision Support System (DSS) that can efficiently detect defects and automate decision-making processes. The system uses a dynamic multi-criteria approach to evaluate possible repair plans and helps manufacturers move closer to Zero Defect Manufacturing.
JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION
(2022)
Article
Computer Science, Interdisciplinary Applications
Deedee Min, Ji-Hyun Lee
Summary: The use of case-based design or decision support systems in ecological designs is limited due to the significant variations in design contexts and variables. Ecological wisdom suggests using evidence-based precedents to avoid ecologically harmful designs, but there is a gap between the concept and its practicality. This research proposes a computational framework for a Precautionary Ecological Planning Assistant, which incorporates design patterns and rules derived from vernacular garden designs to provide data-driven and precautionary support for ecological designs.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2022)
Article
Engineering, Electrical & Electronic
Yisong Wang, Chunyan Wang, Wanzhong Zhao, Can Xu
Summary: This paper proposes a decision-making and planning method for autonomous vehicles based on motivation and risk assessment, which can flexibly adjust path and speed, and make effective driving behavior decisions in real-time environments.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Engineering, Industrial
Bart L. MacCarthy, Robert C. Pasley
Summary: PLM systems are used to support industrial organizations in managing their product portfolios across all phases of the product lifecycle. Decision-making in PLM is an under-researched area, but can be enhanced by applying decision-making theory and group decision support concepts. Six principles have been proposed to support decision-making in a PLM context, enabling decisions to be codified, recorded, and reviewed for future reuse.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Engineering, Industrial
Sandra Hogenboom, Jan Erik Vinnem, Ingrid B. Utne, Trond Kongsvik
Summary: A Dynamic Positioning (DP) system allows vessels and rigs to maintain a predetermined position and heading accurately under harsh environmental conditions. The decisions made by DP operators (DPOs) are safety critical, and their roles are evaluated through applied cognitive task analysis. Recommendations for safety improvement, system design, training, and operations set-up are formulated based on the analysis results.
Article
Forestry
David E. Calkin, Christopher D. O'Connor, Matthew P. Thompson, Richard Stratton
Summary: The USDA Forest Service initiated the Risk Management Assistance (RMA) program in 2016 to improve strategic decision-making on large and complex wildfire events. RMA involves personnel from various disciplines to produce actionable science, aligning with best practices in risk assessment and decision-making. Over the years, RMA has evolved in content, structure, and application domain, expanding from large incident support to pre-event assessment and organizational change.
Article
Green & Sustainable Science & Technology
Yang Zhou, Bing Li, Jingcheng Han, Guojian He, Keyi Wang, Chunjiang An, Yuefei Huang
Summary: Increasing water-use efficiency is crucial for sustainable water management, especially in agriculture, which is the dominant sector for water use. This study proposes a multi-objective decision support model tool that considers risks and robustness to explore water-efficient agricultural development schemes and assess their environmental impacts. The tool integrates economic growth, water use objectives, and pollution control requirements to provide insights into the trade-offs between resource efficiency and environmental impact. A case study in a Chinese city demonstrates how the tool can reduce water usage in agriculture while improving economic productivity. The results suggest that stricter phosphorus control requirements can lead to better pollution control in the agricultural system. Overall, this tool showcases the applicability of using a systems analysis approach to guide the transition towards water-efficient and low-impact agricultural practices.
JOURNAL OF CLEANER PRODUCTION
(2023)
Review
Environmental Sciences
Daniel Krewski, Patrick Saunders-Hastings, Patricia Larkin, Margit Westphal, Michael G. Tyshenko, William Leiss, Maurice Dusseault, Michael Jerrett, Doug Coyle
Summary: Risk management decisions in public health require consideration of complex factors, and this review proposes 10 fundamental principles to guide risk decision-making. The applicability of these principles in practice needs to be evaluated based on specific contexts, and decision-makers ultimately need to exercise judgment to make appropriate risk decisions.
JOURNAL OF TOXICOLOGY AND ENVIRONMENTAL HEALTH-PART B-CRITICAL REVIEWS
(2022)
Article
Neurosciences
Nicole L. Jenni, Griffin Rutledge, Stan B. Floresco
Summary: The medial orbitofrontal cortex (mOFC) plays a role in regulating risk/reward decision-making through different projection pathways. The mOFC -> NAc circuit helps establish and stabilize decision biases, while the mOFC -> DMS circuit facilitates adjustments in decision biases based on changes in profitability.
JOURNAL OF NEUROSCIENCE
(2022)
Article
Environmental Sciences
Cian Kelly, Finn Are Michelsen, Karl Johan Reite, Jeppe Kolding, Oystein Varpe, Are Prytz Berset, Morten Omholt Alver
Summary: There is growing interest in utilizing fishers' knowledge to understand the marine environment. However, integrating such information into advisory processes is challenging. The development of a decision support tool can incentivize data sharing and support sustainable fishing activity and knowledge exchange between research and fishing activity. Such collaborations can rapidly generate extensive datasets and improve the understanding of the oceans, fish stocks, and fishing activities.
FRONTIERS IN MARINE SCIENCE
(2022)
Article
Construction & Building Technology
Hamidreza Alavi, Rafaela Bortolini, Nuria Forcada
Summary: This paper proposes a data model to integrate the building condition risk assessment model into BIM, and tests and evaluates it through a case study. Addressing interoperability issues can automate the data transfer process, improve consistency and reliability, and make BIM a more effective tool for building analysis.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Environmental Sciences
Current Masunungure, Amanda Manyani, Mwazvita T. B. Dalu, Agripa Ngorima, Tatenda Dalu
Summary: Invasive alien species pose a global threat to economies and biodiversity. Existing tools to assist in decision-making for IAS management are inadequate. There is a need for simple decision support tools (DST) that guide stakeholders based on objective and quantifiable criteria. A literature review shows an increase in availability of DST, but with biases in geographical, habitat, and taxonomic focus. Most existing tools do not apply principles of robust decision-making. More consideration and adoption of these principles are needed to improve the applicability of DST for IAS invasions.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)