Performance comparison between KNN and NSGA-II algorithms as calibration approaches for building simulation models

Shadi Basurra , and Ljubomir Jankovic, ‘Performance comparison between KNN and NSGA-II algorithms as calibration approaches for building simulation models’, in BSO 2016 Proceedings. Paper presented at the 3rd IBPSA England Conference, Newcastle, September 2016. Content in the UH Research Archive is made available for personal research, educational, and non-commercial purposes only. Unless otherwise stated, all content is protected by copyright, and in the absence of an open license, permissions for further re-use should be sought from the publisher, the author, or other copyright holder.In this paper, a study of calibration methods for a thermal performance model of a building is presented. Two calibration approaches are evaluated and compared in terms of accuracy and computation speed. These approaches are the 푘 Nearest Neighbour (KNN) algorithm and NSGA-II algorithm. The comparison of these two approaches was based on the simulation model of the Birmingham Zero Carbon House, which has been under continuous monitoring over the past five years. Data from architectural drawings and site measurements were used to build the geometry of the house. All building systems, fabric, lighting and equipment were specified to closely correspond to the actual house. The preliminary results suggest that the predictive performance of simulation models can be calibrated quickly and accurately using the monitored performance data of the real building. Automating such process increases its efficiency and consistency of the results while reducing the time and effort required for calibration. The results show that both NSGA-II and KNN provide similar degree of accuracy in terms of the results closeness to measured data, but whilst the former outperforms the latter in terms of computational speed, the latter outperforms the former in terms of results wide coverage of solutions around the reference point, which is essential for calibration.Final Published versio

Social-aware routing for wireless mesh networks

In wireless mesh networks (WMN), most routing algorithms apply broadcasting at some stage of the path discovery process. They thereby consume large chunks of the network throughput. Intelligent rebroadcast algorithms aim to reduce this overhead by calculating the usefulness of a rebroadcast and the likelihood of collisions. Unfortunately, this introduces latency and breaks the rebroadcast chain, resulting in reduced reachability. In this paper we present our Social-aware Routing Protocol with Parallel Collision Guidance Broadcasting for WMN (SCG). It reduces rebroadcasting without a loss in reachability and without a significant increase in latency. Our claims are validated through simulations comparing our algorithm with existing protocols

Internet of Things and data mining: from applications to techniques and systems

The Internet of Things (IoT) is the result of the convergence of sensing, computing, and networking technologies, allowing devices of varying sizes and computational capabilities (things) to intercommunicate. This communication can be achieved locally enabling what is known as edge and fog computing, or through the well‐established Internet infrastructure, exploiting the computational resources in the cloud. The IoT paradigm enables a new breed of applications in various areas including health care, energy management and smart cities. This paper starts off with reviewing these applications and their potential benefits. Challenges facing the realization of such applications are then discussed. The sheer amount of data stemmed from devices forming the IoT requires new data mining systems and techniques that are discussed and categorized later in this paper. Finally, the paper is concluded with future research directions

DenseNet-201 and Xception pre-trained deep learning models for fruit recognition

With the dramatic increase of the global population and with food insecurity increasing, it has become a major concern for both individuals and governments to fulfill the need for foods such as vegetables and fruits. Moreover, the desire for the consumption of healthy food, including fruit, has increased the need for applications in the field of agriculture that help to achieve better methods for fruit sorting and fruit disease prediction and classification. Automated fruit recognition is a potential solution to reduce the time and labor required to identify different fruits in situations such as retail stores during checkout, fruit processing centers during sorting, and orchards during harvest. Automating these processes reduces the need for human intervention, making them cheaper, faster, and immune to human error and biases. Past research in the field has focused mainly on the size, shape, and color features of fruits or employed convolutional neural networks (CNNs) for their classification. This study investigates the effectiveness of pre-trained deep learning models for fruit classification using two distinct datasets: Fruits-360 and the Fruit Recognition dataset. Four pre-trained models, DenseNet-201, Xception, MobileNetV3-Small, and ResNet-50, were chosen for the experiments based on their architecture and features. The results show that all models achieved almost 99% accuracy or higher with Fruits-360. With the Fruit Recognition dataset, DenseNet-201 and Xception achieved accuracies of around 98%. The good results exhibited by DenseNet-201 and Xception on both the datasets are remarkable, with DenseNet-201 attaining accuracies of 99.87% and 98.94%, and Xception attaining 99.13% and 97.73% accuracy, respectively, on Fruits-360 and the Fruit Recognition dataset

Laser communications system with drones as relay medium for healthcare applications

This article introduces a prototype laser communication system integrated with uncrewed aerial vehicles (UAVs), aimed at enhancing data connectivity in remote healthcare applications. Traditional radio frequency systems are limited by their range and reliability, particularly in challenging environments. By leveraging UAVs as relay points, the proposed system seeks to address these limitations, offering a novel solution for real-time, high-speed data transmission. The system has been empirically tested, showcasing its ability to maintain data transmission integrity under various conditions. Results indicate a substantial improvement in connectivity, with high data transmission success rate (DTSR) scores, even amidst environmental disturbances. This study underscores the system’s potential for critical applications such as emergency response, public health monitoring, and extending services to remote or underserved areas

Factors in the emergence and sustainability of self-regulation.

Please help populate SUNScholar with the full text of SU research output. Also - should you need this item urgently, please snd us the details and we will try to get hold of the full text as quick possible. E-mail to [email protected]. Thank you.Ekonomiese En BestuurswetenskappeNagraadse Bestuurskoo

Consumption practices and perception of ready-to-eat food among university students and employees in Kuala Lumpur, Malaysia

The purpose of the study was to examine the consumption practices and perception of ready-to-eat food among university students and employees in Kuala Lumpur, Malaysia. Through random sampling, a total of ninety-three respondents participated by answering questionnaires. The results showed that majority of the respondents (52%) consumed RTE food two to four times a week, and most of them (44%) consumed RTE food during lunch. The biggest motivator for the respondents to purchase RTE food was convenience (46%). It could be highlighted that majority of the respondents felt that fast-food restaurants to be very safe (11.8%), and that street foods to be very risky (34.4%). Most of the respondents were very worried about human spread diseases (such as Hepatitis B) and human spread bacteria (such as E. coli) when buying food (43%). When buying RTE food, consumers were most concerned about the cleanliness of the store they were buying their food from (66.7%). The present study indicated that university students and employees showed food safety awareness and concerns especially regarding RTE food. This study could benefit food marketers, and also public health organizations in their efforts to develop more effective education and dissemination of information to the public

Diabetes disease prediction system using HNB classifier based on discretization method

Diagnosing diabetes early is critical as it helps patients live with the disease in a healthy way – through healthy eating, taking appropriate medical doses, and making patients more vigilant in their movements/activities to avoid wounds that are difficult to heal for diabetic patients. Data mining techniques are typically used to detect diabetes with high confidence to avoid misdiagnoses with other chronic diseases whose symptoms are similar to diabetes. Hidden Naïve Bayes is one of the algorithms for classification, which works under a data-mining model based on the assumption of conditional independence of the traditional Naïve Bayes. The results from this research study, which was conducted on the Pima Indian Diabetes (PID) dataset collection, show that the prediction accuracy of the HNB classifier achieved 82%. As a result, the discretization method increases the performance and accuracy of the HNB classifier