The balancing of demand and supply in the market is a complex problem because of the dynamic supply chain and environment. It causes uncertain situations and is a limitation in decisions making systems that cannot produce reasonable descriptions to help decision makers eliminate uncertainties. This study proposes the design and development of a Causal Bayesian Networks (CBNs) model for market understanding, which encodes a human-like approach to explain demand and supply events for decision makers. A framework for generating reasonable descriptions in Agricultural Supply Chains (ASCs) management is proposed. The qualitative and quantitative design of the CBNs model is developed and proved that the CBNs model can reasonably explain events using predictive performance measurement and sensitivity analysis for producing reasonable descriptions. The results illustrate that the CBNs model is suitable for ASCs situation explanation involving uncertain situations, and is ready to apply to real-world applications to support decision making systems.
Controlling thermal comfort in the indoor environment demands research because it is fundamental to indicating occupants’ health, wellbeing, and performance in working productivity. A suitable thermal comfort must monitor and balance complex factors from heating, ventilation, air-conditioning systems (HVAC Systems) and outdoor and indoor environments based on advanced technology. It needs engineers and technicians to observe relevant factors on a physical site and to detect problems using their experience to fix them early and prevent them from worsening. However, it is a labor-intensive and time-consuming task, while experts are short on diagnosing and producing proactive plans and actions. This research addresses the limitations by proposing a new Internet of Things (IoT)-driven fault detection system for indoor thermal comfort. We focus on the well-known problem caused by an HVAC system that cannot transfer heat from the indoor to outdoor and needs engineers to diagnose such concerns. The IoT device is developed to observe perceptual information from the physical site as a system input. The prior knowledge from existing research and experts is encoded to help systems detect problems in the manner of human-like intelligence. Three standard categories of machine learning (ML) based on geometry, probability, and logical expression are applied to the system for learning HVAC system problems. The results report that the MLs could improve overall performance based on prior knowledge around 10% compared to perceptual information. Well-designed IoT devices with prior knowledge reduced false positives and false negatives in the predictive process that aids the system to reach satisfactory performance.
Indoor thermal comfort affects occupants’ daily activities and health. HVAC systems are necessary to control thermal comfort quality. Tracking and monitoring the effectiveness of HVAC system engines are critical activities because they ensure that the system can produce suitable indoor thermal comfort. However, the operation of such systems depends on practitioners and engineers, which is time-consuming and labor-intensive. Moreover, installing physical sensors into the system engine may keep track of the problem but may also require costs and maintenance. This research addressed this concern by presenting deep learning (DL)-driven automated fault detection and diagnostics (AFDD) for HVAC systems. It employed contextual factors as an indirect measurement to avoid modifying HVAC system engines (e.g., according to standard building appliance warranties) but was still able to effectively detect issues. The design and development of the DL model are proposed to encode complex behaviors of an HVAC system using contextual factors. The experimental results show that the predictive performance of our model achieved an average F-measure of over 97%, which was outstanding compared with the standard ML models. This proposed model will be a natural fit for AFDD for HVAC systems and is ready for future real-world applications as required by building engineering.
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