Power Systems Monitoring and Control using Telecom Network Management Standards

Historically, different solutions have been developed for power systems control and telecommunications network management environments. The former was characterized by proprietary solutions, while the latter has been involved for years in a strong standardization process guided by criteria of openness. Today, power systems control standardization is in progress, but it is at an early stage compared to the telecommunications management area, especially in terms of information modeling. Today, control equipment tends to exhibit more computational power, and communication lines have increased their performance. These trends hint at some conceptual convergence between power systems and telecommunications networks from a management perspective. This convergence leads us to suggest the application of well-established telecommunications management standards for power systems control. This paper shows that this is a real medium-to-long term possibility

OGC SWE-based Data Acquisition System Development for EGIM on EMSODEV EU Project

The EMSODEV[1] (European Multidisciplinary Seafloor and water column Observatory DEVelopment) is an EU project whose general objective is to set up the full implementation and operation of the EMSO distributed Research Infrastructure (RI), through the development, testing and deployment of an EMSO Generic Instrument Module (EGIM). This research infrastructure will provide accurate records on marine environmental changes from distributed local nodes around Europe. These observations are critical to respond accurately to the social and scientific challenges such as climate change, changes in marine ecosystems, and marine hazards. In this paper we present the design and development of the EGIM data acquisition system. EGIM is able to operate on any EMSO node, mooring line, sea bed station, cabled or non-cabled and surface buoy. In fact a central function of EGIM within the EMSO infrastructure is to have a number of ocean locations where the same set of core variables are measured homogeneously: using the same hardware, same sensor references, same qualification methods, same calibration methods, same data format and access, and same maintenance procedures.Peer ReviewedPostprint (published version

Energy Information Systems: From the Basement to the Boardroom

A significant buildings energy reduction opportunity exists in the office sector, given that this market segment typically is an early adopter of new technology. There is a rising trend towards smart and connected offices through the internet of things (IoT) that provides new opportunities for operational efficiency and environmental sustainability practices. Leading commercial real estate companies have begun to shift from individual building automation systems (BAS) to partially integrated and automated systems such as energy information systems (EIS). In both the United States and India, organizations are seeking operational excellence, enhanced tenant relationships, and topline growth. Hence it is imperative to engage the executives with decision-making power, by tapping into their interest in sustainability, corporate social responsibility, and innovation. This expansion of interest can enable data-driven decisions, strong energy investments, and deeper energy benefits, and would drive innovation in this field. However, none of this would be possible without robust, consistent building energy information to provide visibility across all the levels of decision making, i.e. from the basement where the facilities staff take operational action to the boardroom where the executives make investment decisions. Price, security, and ease of use remain barriers to the adoption and pervasive use of promising EIS technologies in commercial office buildings. We believe that these barriers can be addressed through the development of ready, simplified, consistent, commercially available, low-cost EIS-in-a-box packages, that have a pre-defined set of hardware components and software features and functionality that are pertinent to a particular building sector. These simplified, sector-specific EIS packages can help to obviate the need for customization, and enhance ease of use, thereby enabling scale-up, in order to facilitate building energy savings. The EIS-in-a-box are adaptable in both U.S. and Indian office buildings, and potentially beyond these two countries

Mission Control Center enhancement opportunities in the 1990's

The purpose of this paper is to present a framework for understanding the major enhancement opportunities for Air Force Mission Control Center/Test Support Centers (MCC's/TSC's) in the 1990's. Much of this paper is based on the findings of Study 232 and work currently underway in Study 2-6 for the Air Force Systems Command, Space System Division, Network Program Office. In this paper, we will address MCC/TSC enhancement needs primarily from the operator perspective, in terms of the increased capabilities required to improve space operations task performance

ARGES: an Expert System for Fault Diagnosis Within Space-Based ECLS Systems

ARGES (Atmospheric Revitalization Group Expert System) is a demonstration prototype expert system for fault management for the Solid Amine, Water Desorbed (SAWD) CO2 removal assembly, associated with the Environmental Control and Life Support (ECLS) System. ARGES monitors and reduces data in real time from either the SAWD controller or a simulation of the SAWD assembly. It can detect gradual degradations or predict failures. This allows graceful shutdown and scheduled maintenance, which reduces crew maintenance overhead. Status and fault information is presented in a user interface that simulates what would be seen by a crewperson. The user interface employs animated color graphics and an object oriented approach to provide detailed status information, fault identification, and explanation of reasoning in a rapidly assimulated manner. In addition, ARGES recommends possible courses of action for predicted and actual faults. ARGES is seen as a forerunner of AI-based fault management systems for manned space systems

MobileTrust: Secure Knowledge Integration in VANETs

Vehicular Ad hoc NETworks (VANET) are becoming popular due to the emergence of the Internet of Things and ambient intelligence applications. In such networks, secure resource sharing functionality is accomplished by incorporating trust schemes. Current solutions adopt peer-to-peer technologies that can cover the large operational area. However, these systems fail to capture some inherent properties of VANETs, such as fast and ephemeral interaction, making robust trust evaluation of crowdsourcing challenging. In this article, we propose MobileTrust—a hybrid trust-based system for secure resource sharing in VANETs. The proposal is a breakthrough in centralized trust computing that utilizes cloud and upcoming 5G technologies to provide robust trust establishment with global scalability. The ad hoc communication is energy-efficient and protects the system against threats that are not countered by the current settings. To evaluate its performance and effectiveness, MobileTrust is modelled in the SUMO simulator and tested on the traffic features of the small-size German city of Eichstatt. Similar schemes are implemented in the same platform to provide a fair comparison. Moreover, MobileTrust is deployed on a typical embedded system platform and applied on a real smart car installation for monitoring traffic and road-state parameters of an urban application. The proposed system is developed under the EU-founded THREAT-ARREST project, to provide security, privacy, and trust in an intelligent and energy-aware transportation scenario, bringing closer the vision of sustainable circular economy

Applying machine learning to predict future adherence to physical activity programs.

BackgroundIdentifying individuals who are unlikely to adhere to a physical exercise regime has potential to improve physical activity interventions. The aim of this paper is to develop and test adherence prediction models using objectively measured physical activity data in the Mobile Phone-Based Physical Activity Education program (mPED) trial. To the best of our knowledge, this is the first to apply Machine Learning methods to predict exercise relapse using accelerometer-recorded physical activity data.MethodsWe use logistic regression and support vector machine methods to design two versions of a Discontinuation Prediction Score (DiPS), which uses objectively measured past data (e.g., steps and goal achievement) to provide a numerical quantity indicating the likelihood of exercise relapse in the upcoming week. The respective prediction accuracy of these two versions of DiPS are compared, and then numerical simulation is performed to explore the potential of using DiPS to selectively allocate financial incentives to participants to encourage them to increase physical activity.Resultswe had access to a physical activity trial data that were continuously collected every 60 sec every day for 9 months in 210 participants. By using the first 15 weeks of data as training and test on weeks 16-30, we show that both versions of DiPS have a test AUC of 0.9 with high sensitivity and specificity in predicting the probability of exercise adherence. Simulation results assuming different intervention regimes suggest the potential benefit of using DiPS as a score to allocate resources in physical activity intervention programs in reducing costs over other allocation schemes.ConclusionsDiPS is capable of making accurate and robust predictions for future weeks. The most predictive features are steps and physical activity intensity. Furthermore, the use of DiPS scores can be a promising approach to determine when or if to provide just-in-time messages and step goal adjustments to improve compliance. Further studies on the use of DiPS in the design of physical activity promotion programs are warranted.Trial registrationClinicalTrials.gov NCT01280812 Registered on January 21, 2011

IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support

In response to the burgeoning global demand for seafood and the challenges of managing fish farms, we introduce an innovative IoT-based environmental control system that integrates sensor technology and advanced machine learning decision support. Deploying a network of wireless sensors within the fish farm, we continuously collect real-time data on crucial environmental parameters, including water temperature, pH levels, humidity, and fish behavior. This data undergoes meticulous preprocessing to ensure its reliability, including imputation, outlier detection, feature engineering, and synchronization. At the heart of our system are four distinct machine learning algorithms: Random Forests predict and optimize water temperature and pH levels for the fish, fostering their health and growth; Support Vector Machines (SVMs) function as an early warning system, promptly detecting diseases and parasites in fish; Gradient Boosting Machines (GBMs) dynamically fine-tune the feeding schedule based on real-time environmental conditions, promoting resource efficiency and fish productivity; Neural Networks manage the operation of critical equipment like water pumps and heaters to maintain the desired environmental conditions within the farm. These machine learning algorithms collaboratively make real-time decisions to ensure that the fish farm's environmental conditions align with predefined specifications, leading to improved fish health and productivity while simultaneously reducing resource wastage, thereby contributing to increased profitability and sustainability. This research article showcases the power of data-driven decision support in fish farming, promising to meet the growing demand for seafood while emphasizing environmental responsibility and economic viability, thus revolutionizing the future of fish farming