Visible to the public Biblio

Filters: Keyword is temperature  [Clear All Filters]
Banakar, V., Upadhya, P., Keshavan, M..  2020.  CIED - rapid composability of rack scale resources using Capability Inference Engine across Datacenters. 2020 IEEE Infrastructure Conference. :1–4.
There are multiple steps involved in transitioning a server from the factory to being fully provisioned for an intended workload. These steps include finding the optimal slot for the hardware and to compose the required resources on the hardware for the intended workload. There are many different factors that influence the placement of server hardware in the datacenter, such as physical limitations to connect to a network be it Ethernet or storage networks, power requirements, temperature/cooling considerations, and physical space, etc. In addition to this, there may be custom requirements driven by workload policies (such as security, data privacy, power redundancy, etc.). Once the server has been placed in the right slot it needs to be configured with the appropriate resources for the intended workload. CIED will provide a ranked list of locations for server placement based on the intended workload, connectivity and physical requirements of the server. Once the server is placed in the suggested slot, the solution automatically discovers the server and composes the required resources (compute, storage and networks) for running the appropriate workload. CIED reduces the overall time taken to move hardware from factory to production and also maximizes the server hardware utilization while minimizing downtime by physically placing the resources optimally. From the case study that was undertaken, the time taken to transition a server from factory to being fully provisioned was proportional to the number of devices in the datacenter. With CIED this time is constant irrespective of the complexity or the number of devices in a datacenter.
Guri, Mordechai.  2019.  HOTSPOT: Crossing the Air-Gap Between Isolated PCs and Nearby Smartphones Using Temperature. 2019 European Intelligence and Security Informatics Conference (EISIC). :94—100.
Air-gapped computers are hermetically isolated from the Internet to eliminate any means of information leakage. In this paper we present HOTSPOT - a new type of airgap crossing technique. Signals can be sent secretly from air-gapped computers to nearby smartphones and then on to the Internet - in the form of thermal pings. The thermal signals are generated by the CPUs and GPUs and intercepted by a nearby smartphone. We examine this covert channel and discuss other work in the field of air-gap covert communication channels. We present technical background and describe thermal sensing in modern smartphones. We implement a transmitter on the computer side and a receiver Android App on the smartphone side, and discuss the implementation details. We evaluate the covert channel and tested it in a typical work place. Our results show that it possible to send covert signals from air-gapped PCs to the attacker on the Internet through the thermal pings. We also propose countermeasures for this type of covert channel which has thus far been overlooked.
Berscheid, A., Makarov, Y., Hou, Z., Diao, R., Zhang, Y., Samaan, N., Yuan, Y., Zhou, H..  2018.  An Open-Source Tool for Automated Power Grid Stress Level Prediction at Balancing Authorities. 2018 IEEE/PES Transmission and Distribution Conference and Exposition (T D). :1–5.
The behavior of modern power systems is becoming more stochastic and dynamic, due to the increased penetration of variable generation, demand response, new power market structure, extreme weather conditions, contingencies, and unexpected events. It is critically important to predict potential system operational issues so that grid planners and operators can take preventive actions to mitigate the impact, e.g., lack of operational reserves. In this paper, an innovative software tool is presented to assist power grid operators in a balancing authority in predicting the grid stress level over the next operating day. It periodically collects necessary information from public domain such as weather forecasts, electricity demand, and automatically estimates the stress levels on a daily basis. Advanced Neural Network and regression tree algorithms are developed as the prediction engines to achieve this goal. The tool has been tested on a few key balancing authorities and successfully predicted the growing system peak load and increased stress levels under extreme heat waves.
Hernández, S., Lu, P. L., Granz, S., Krivosik, P., Huang, P. W., Eppler, W., Rausch, T., Gage, E..  2017.  Using Ensemble Waveform Analysis to Compare Heat Assisted Magnetic Recording Characteristics of Modeled and Measured Signals. IEEE Transactions on Magnetics. 53:1–6.

Ensemble waveform analysis is used to calculate signal to noise ratio (SNR) and other recording characteristics from micromagnetically modeled heat assisted magnetic recording waveforms and waveforms measured at both drive and spin-stand level. Using windowing functions provides the breakdown between transition and remanence SNRs. In addition, channel bit density (CBD) can be extracted from the ensemble waveforms using the di-bit extraction method. Trends in both transition SNR, remanence SNR, and CBD as a function of ambient temperature at constant track width showed good agreement between model and measurement. Both model and drive-level measurement show degradation in SNR at higher ambient temperatures, which may be due to changes in the down-track profile at the track edges compared with track center. CBD as a function of cross-track position is also calculated for both modeling and spin-stand measurements. The CBD widening at high cross-track offset, which is observed at both measurement and model, was directly related to the radius of curvature of the written transitions observed in the model and the thermal profiles used.

Sándor, H., Genge, B., Szántó, Z..  2017.  Sensor data validation and abnormal behavior detection in the Internet of Things. 2017 16th RoEduNet Conference: Networking in Education and Research (RoEduNet). :1–5.
Internet of Things (IoT) and its various application domains are radically changing the lives of people, providing smart services which will ultimately constitute integral components of the living environment. The services of IoT operate based on the data flows collected from the different sensors and actuators. In this respect, the correctness and security of the sensor data transported over the IoT system is a crucial factor in ensuring the correct functioning of the IoT services. In this work, we present a method that can detect abnormal sensor events based on “apriori” knowledge of the behavior of the monitored process. The main advantage of the proposed methodology is that it builds on well-established theoretical works, while delivering a practical technique with low computational requirements. As a result, the developed technique can be hosted on various components of an IoT system. The developed approach is evaluated through real-world use-cases.
Zhang, Q., Ma, Z., Li, G., Qian, Z., Guo, X..  2016.  Temperature-dependent demagnetization nonlinear Wiener model with neural network for PM synchronous machines in electric vehicle. 2016 19th International Conference on Electrical Machines and Systems (ICEMS). :1–4.

The inevitable temperature raise leads to the demagnetization of permanent magnet synchronous motor (PMSM), that is undesirable in the application of electrical vehicle. This paper presents a nonlinear demagnetization model taking into account temperature with the Wiener structure and neural network characteristics. The remanence and intrinsic coercivity are chosen as intermediate variables, thus the relationship between motor temperature and maximal permanent magnet flux is described by the proposed neural Wiener model. Simulation and experimental results demonstrate the precision of temperature dependent demagnetization model. This work makes the basis of temperature compensation for the output torque from PMSM.