Smart Sensors and Devices in Artificial Intelligence

Zhang, Dan; Wei, Bin

doi:10.3390/s20205945

Cited by 7 publications

(2 citation statements)

References 18 publications

(18 reference statements)

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“…It has been proven that such technologies increase the yields by appropriate assessment of the quality [41][42][43][44][45], and then classification of the yields, which significantly influences the optimization of production [46][47][48][49][50]. The use of intelligent sensors [51][52][53][54][55][56][57] and advanced techniques of artificial intelligence [55,58] enable the planning [59][60][61] and analysis of agricultural production. This approach creates integrated monitoring systems in plant production, as well as in animal husbandry [62].…”

Section: Smart Agriculturementioning

confidence: 99%

BelBuk System—Smart Logistics for Sustainable City Development in Terms of the Deficit of a Chemical Fertilizers

Grunt¹,

Błażejewski

Pecolt

et al. 2022

Energies

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Purpose: This paper presents an aspect of asset tracking and storage conditions. This paper aims to fill the gap in the development of Industry 4.0 in terms of fully digital asset tracking to be implemented by medium and large-size manufacturing and logistics facilities. The article presents an innovative technology for the remote monitoring of chemical raw materials, including fertilizers, during their storage and transport from the place of manufacture to the local distributor or recipient. Methods: The method assumes the monitoring and identification of special transport bags, so-called “big-bags,” through embedded RFID tags or LEB labels and monitoring the key parameters of their content, i.e., temperature, humidity, insolation, and pressure, using a measuring micro-station that is placed in the transported raw material. Results: The automation of inference based on the collected information about the phenomenon in question (the distribution of parameters: pressure, temperature, and humidity), and expert knowledge, allows the creation of an advisory system prototype indicating how to manage the measuring devices. Conclusions: No similar solution in the field of monitoring environmental parameters has been implemented in the Polish market. The developed system enables the monitoring of 10,000 pieces of big bags in at least 30 locations simultaneously.

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Section: Smart Agriculturementioning

confidence: 99%

BelBuk System—Smart Logistics for Sustainable City Development in Terms of the Deficit of a Chemical Fertilizers

Grunt¹,

Błażejewski

Pecolt

et al. 2022

Energies

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“…USSNs are composed of artificial intelligence (AI) and several specialized sensors [8]. This demonstrates how Smart Sensors are connected to various Smart Fusion Servers with AI Databases [9] through the use of Distributed Fusion Architecture (Figure 1) [10] to carry out multiple tasks like collecting, processing, and storing data continuously and permanently from various sensors dispersed throughout various locations within the same network.…”

Section: Introductionmentioning

confidence: 99%

Environmental Constraints for Intelligent Internet of Deep-Sea/Underwater Things Relying on Enterprise Architecture Approach

Aoun,

Mansour,

Dornaika

et al. 2024

Sensors

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Through the use of Underwater Smart Sensor Networks (USSNs), Marine Observatories (MOs) provide continuous ocean monitoring. Deployed sensors may not perform as intended due to the heterogeneity of USSN devices’ hardware and software when combined with the Internet. Hence, USSNs are regarded as complex distributed systems. As such, USSN designers will encounter challenges throughout the design phase related to time, complexity, sharing diverse domain experiences (viewpoints), and ensuring optimal performance for the deployed USSNs. Accordingly, during the USSN development and deployment phases, a few Underwater Environmental Constraints (UECs) should be taken into account. These constraints may include the salinity level and the operational depth of every physical component (sensor, server, etc.) that will be utilized throughout the duration of the USSN information systems’ development and implementation. To this end, in this article we present how we integrated an Artificial Intelligence (AI) Database, an extended ArchiMO meta-model, and a design tool into our previously proposed Enterprise Architecture Framework. This addition proposes adding new Underwater Environmental Constraints (UECs) to the AI Database, which is accessed by USSN designers when they define models, with the goal of simplifying the USSN design activity. This serves as the basis for generating a new version of our ArchiMO design tool that includes the UECs. To illustrate our proposal, we use the newly generated ArchiMO to create a model in the MO domain. Furthermore, we use our self-developed domain-specific model compiler to produce the relevant simulation code. Throughout the design phase, our approach contributes to the handling and controling of the uncertainties and variances of the provided quality of service that may occur during the performance of the USSNs, as well as reducing the design activity’s complexity and time. It provides a way to share the different viewpoints of the designers in the domain of USSNs.

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Synthesis of classification and predictive FFNN models on the basis of tenzoresistive transducer data

Balabanova¹,

Sadinov²,

Daskalaki³

et al. 2022

The 9TH INTERNATIONAL CONFERENCE OF THE INDONESIAN CHEMICAL SOCIETY ICICS 2021: Toward a Meaningful Society

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Smart Sensors and Devices in Artificial Intelligence

Abstract: As stated in the Special Issue call, “sensors are eyes or/and ears of an intelligent system, such as Unmanned Aerial Vehicle (UAV), Automated Guided Vehicle (AGV) and robots [...]

Cited by 7 publications

References 18 publications

BelBuk System—Smart Logistics for Sustainable City Development in Terms of the Deficit of a Chemical Fertilizers

BelBuk System—Smart Logistics for Sustainable City Development in Terms of the Deficit of a Chemical Fertilizers

Environmental Constraints for Intelligent Internet of Deep-Sea/Underwater Things Relying on Enterprise Architecture Approach

Synthesis of classification and predictive FFNN models on the basis of tenzoresistive transducer data

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