Biblio

Building occupancy data helps increase energy management systems’ performance, enabling lower energy use while preserving occupant comfort. The focus of this study is employing environmental data (e.g., including but not limited to temperature, humidity, carbon dioxide (CO2), etc.) to infer occupancy information. This will be achieved by exploring the application of information theory metrics with machine learning (ML) approaches to classify occupancy levels for a given dataset. Three datasets and six distinct ML algorithms were used in a comparative study to determine the best strategy for identifying occupancy patterns. It was determined that both k-nearest neighbors (kNN) and random forest (RF) identify occupancy labels with the highest overall level of accuracy, reaching 97.99% and 98.56%, respectively.

Permanent magnet synchronous motors (PMSM) are widely used in the shafts of industrial robots. The quality consistency of PMSM, derived from both the wide range of operating temperature and inherent uncertainties, significantly influences the application of the PMSM. In this paper, the mechanism of temperature influence on the PMSM is analyzed with the aid of the digital model, and the quantitative relationship between the main PMSM feature, the cogging torque, and the temperature is revealed. Then, the NdFeB remanence in different temperature levels was measured to obtain its temperature coefficient. The finite element method is used to simulate PMSM. The qualitative and quantitative conclusions of cogging torque drop when the temperature rises are verified by experiments. The magnetic performance data of the magnetic tiles of 50 motors were randomly sampled and the cogging torque simulation was carried out under the fixed ambient temperature. The results show that the dispersion significantly increases the stray harmonic components of the cogging torque.

In this paper, we propose a method of stochastic model predictive control for energy management systems including human-in-the-loop. Here, we consider an air-conditioning system consisting of some rooms. Human decision making about the set temperature is modeled by a discrete-time Markov chain. The finite-time optimal control problem solved in the controller is reduced to a mixed integer linear programming problem.

The Covid-19 Pandemic has caused huge losses worldwide and is still affecting people all around the world. Even after rigorous, incessant and dedicated efforts from people all around the world, it keeps mutating and spreading at an alarming rate. In times such as these, it is extremely important to take proper precautionary measures to stay safe and help to contain the spread of the virus. In this paper, we propose an innovative design of one such commonly used public disinfection method, an Automatic Walkthrough Sanitization Tunnel. It is a walkthrough sanitization tunnel which uses sensors to detect the target and automatically disinfects it followed by irradiation using UV-C rays for extra protection. There is a proposition to add an IoT based Temperature sensor and data relay module used to detect the temperature of any person entering the tunnel and in case of any anomaly, contact nearby covid wards to facilitate rapid treatment.

In order to increase the availability and reliability of photovoltaic (PV) systems, fault diagnosis and condition monitoring of inverters are of crucial means to meet the goals. Numerous methods are implemented for fault diagnosis of PV inverters, providing robust features and handling massive amount of data. However, existing methods rely on simplistic frameworks that are incapable of inspecting a wide range of intrinsic and explicit features, as well as being time-consuming. In this paper, a novel method based on a multilayer deep belief network (DBN) is suggested for fault diagnosis, which allows the framework to discover the probabilistic reconstruction across its inputs. This approach equips a robust hierarchical generative model for exploiting features associated with faults, interprets functions that are highly variable, and needs lesser prior information. Moreover, the method instantaneously categorizes the fault conditions, which eventually strengthens the adaptability of applying it on a variety of diagnostic problems in an inverter domain. The proposed method is evaluated using multiple input signals at different sampling frequencies. To evaluate the efficacy of DBN, a test model based on a three-phase 2-level grid-tied PV inverter was used. The results show that the method is capable of achieving precise diagnosis operations.

Wireless sensor networks (WSN) are used to perceive many data such as temperature, vibration, pressure in the environment and to produce results; it is widely used, including in critical fields such as military, intelligence and health. However, because of WSNs have different infrastructure and architecture than traditional networks, different security measures must be taken. In this study, an intrusion detection system (IDS) is modeled to ensure WSN security. Since the signature, misuse and anomaly based detection methods for intrusion detection systems are insufficient to provide security alone, a hybrid model is proposed in which these methods are used together. In the hybrid model, anomaly rules were defined for attack detection, and machine learning algorithms BayesNet, J48 and Random Forest were used to classify normal and abnormal traffic. Unlike the studies in the literature, CSE-CIC-IDS2018, the most up-to-date data set, was used to create attack profiles. Considering both hardware constraints and battery capacities of WSNs; the data was pre-processed in accordance with data mining principles. The results showed that the developed model has high accuracy and low false alarm rate.

This work examines volatile memory modules as ephemeral key storage for security applications in the context of low temperatures. In particular, we note that such memories exhibit a rising level of data remanence as the temperature decreases, especially for temperatures below 280 Kelvin. Therefore, these memories cannot be used to protect the superconducting modules found in high-speed Magnetic Levitation (MagLev) trains, as such modules most often require extremely low temperatures in order to provide superconducting applications. Thus, a novel secure storage solution is required in this case, especially within the oncoming framework concept of the internet of railway things, which is partially based on the increasing utilisation of commercial off-the-shelf components and potential economies of scale, in order to achieve cost efficiency and, thus, widespread adoption. Nevertheless, we do note that volatile memory modules can be utilised as intrinsic temperature sensors, especially at low temperatures, as the data remanence they exhibit at low temperatures is highly dependent on the ambient temperature, and can, therefore, be used to distinguish between different temperature levels.

An important demand of a wearable health observance system is to soundly exchange the Employees' health data and preventing improper use of black devices. In this project we tend to measure planning wearable sensors device sight abnormal and/or unforeseen things by observance physiological parameters alongside different symptoms. Therefore, necessary facilitate is provided in times of urgent would like. To minimize the health hazards and improving the well-being of employees is to be a major critical role in an organization. As per the report by the Indian Labour Organization, the organization spends an average of 3.94% for GDP on employee treatment. The same study revealed that almost 2.78% million deaths occurs every year and 3.74% million occur non-fatal injuries every year at work. So, the organizations are making towards mitigating the facilities to decimating various IoT technologies and the IoT technology are embedded with modern smart systems, it is easy to monitor every employee in an organization, and also it collects and gather the data and send any critical information by the employees.

In case of wildfire, particles generated in combustion are in complex law of motion under the influence of flame temperature, airflow and lots of electrons and ions. They would distort the space electric field, and lead to gap discharge. This paper develops a multi-physics coupling calculation model of fluid, temperature, electric field and particle movement by combining the rod-plate gap experiment that simulates the wildfire condition. It analyzes the motion state of ash particles in flames, studies the charged particles of different polarity separately, and explores the impact of particle properties on the electric field of gap space by combining the distribution of particles. Results have shown that there are differences in the motion state of charged particles of different polarity, and the electrode will absorb some particles with different charges, while charged particles with the same polarity as the electrode will move away from the electrode in random motion. Particles of different properties (particle size, relative dielectric constant) have different impacts on the electric field of gap space, but they all promote the discharge propagation.

In this article, the writers suggested a scheme for analyzing the optimum crop cultivation based on Fuzzy Logic Network (Implementation of Fuzzy Logic Control in Predictive Analysis and Real Time Monitoring of Optimum Crop Cultivation) knowledge. The Fuzzy system is Fuzzy Logic's set. By using the soil, temperature, sunshine, precipitation and altitude value, the scheme can calculate the output of a certain crop. By using this scheme, the writers hope farmers can boost f arm output. This, thus will have an enormous effect on alleviating economical deficiency, strengthening rate of employment, the improvement of human resources and food security.

Trust of image sensor data is becoming increasingly important as the Internet of Things (IoT) applications grow from home appliances to surveillance. Up to our knowledge, there exists only one work in literature that estimates trustworthiness of digital images applied to forensic applications, based on a machine learning technique. The efficacy of this technique is heavily dependent on availability of an appropriate training set and adequate variation of IoT sensor data with noise, interference and environmental condition, but availability of such data cannot be assured always. Therefore, to overcome this limitation, a robust method capable of estimating trustworthy measure with high accuracy is needed. Lowering cost of sensors allow many IoT applications to use multiple types of sensors to observe the same event. In such cases, complementary multimodal data of one sensor can be exploited to measure trust level of another sensor data. In this paper, for the first time, we introduce a completely new approach to estimate the trustworthiness of an image sensor data using another sensor's numerical data. We develop a theoretical model using the Dempster-Shafer theory (DST) framework. The efficacy of the proposed model in estimating trust level of an image sensor data is analyzed by observing a fire event using IoT image and temperature sensor data in a residential setup under different scenarios. The proposed model produces highly accurate trust level in all scenarios with authentic and forged image data.

In view of the problem that the health status and safety monitoring of the traditional intelligent home are mainly dependent on the manual inspection, this paper introduces the intelligent home-based remote monitoring system by introducing the Internet-based Internet of Things technology into the intelligent home condition monitoring and safety assessment. The system's Android remote operation based on the MVP model to develop applications, the use of neural networks to deal with users daily use of operational data to establish the network data model, combined with S3C2440A microcontrollers in the gateway to the embedded Linux to facilitate different intelligent home drivers development. Finally, the power line communication network is used to connect the intelligent electrical appliances to the gateway. By calculating the success rate of the routing nodes, the success rate of the network nodes of 15 intelligent devices is 98.33%. The system can intelligent home many electrical appliances at the same time monitoring, to solve the system data and network congestion caused by the problem can not he security monitoring.

This paper investigates the temperature demagnetization modeling method for permanent magnets (PM) in permanent magnet synchronous linear motor (PMSLM). First, the PM characteristics are presented, and finite element analysis (FEA) is conducted to show the magnetic distribution under different temperatures. Second, demagnetization degrees and remanence of the five PMs' experiment sample are actually measured in stove at temperatures varying from room temperature to 300 °C, and to obtain the real data for next-step modeling. Third, machine learning algorithm called extreme learning machine (ELM) is introduced to map the nonlinear relationships between temperature and demagnetization characteristics of PM and build the demagnetization models. Finally, comparison experiments between linear modeling method, polynomial modeling method, and ELM can certify the effectiveness and advancement of this proposed method.

In this paper, machine learning attacks are performed on a novel hybrid delay based Arbiter Ring Oscillator PUF (AROPUF). The AROPUF exhibits improved results when compared to traditional Arbiter Physical Unclonable Function (APUF). The challenge-response pairs (CRPs) from both PUFs are fed to the multilayered perceptron model (MLP) with one hidden layer. The results show that the CRPs generated from the proposed AROPUF has more training and prediction errors when compared to the APUF, thus making it more difficult for the adversary to predict the CRPs.

The goal of this letter is to explore the extent to which the vulnerabilities plaguing the Internet, particularly susceptibility to distributed denial-of-service (DDoS) attacks, impact the Cloud. DDoS has been known to disrupt Cloud services, but could it do worse by permanently damaging server and switch hardware? Services are hosted in data centers with thousands of servers generating large amounts of heat. Heating, ventilation, and air-conditioning (HVAC) systems prevent server downtime due to overheating. These are remotely managed using network management protocols that are susceptible to network attacks. Recently, Cloud providers have experienced outages due to HVAC malfunctions. Our contributions include a network simulation to study the feasibility of such an attack motivated by our experiences of such a security incident in a real data center. It demonstrates how a network simulator can study the interplay of the communication and thermal properties of a network and help prevent the Cloud provider's worst nightmare: meltdown of the data center as a result of a DDoS attack.