Enhancement of Shock Absorption Using Hybrid SMA-MRF Damper by Complementary Operation

A hybrid damper concept is presented here using a combination of a Magnetorheological (MR) Fluid (MRF) and Shape Memory Alloy (SMA)-based energy dissipation. A demonstration is performed utilizing the shear operating mode of the MRF and the one-way effect of the SMA. The damping performance of different MRF-SMA configurations is investigated and the corresponding energy consumption is evaluated. We demonstrate that the operation of MRF and SMA dampers complement each other, compensating for each other’s weaknesses. In particular, the slow response from the MR damper is compensated by passive SMA damping using the pseudoplastic effect of martensite reorientation, which can dissipate a significant amount of shock energy at the beginning of the shock occurrence. The MR damper compensates for the incapability of the SMA to dampen subsequent vibrations as long as the magnetic field is applied. The presented hybrid SMA-MR damper demonstrates superior performance compared to individual dampers, allowing for up to five-fold reduction in energy consumption of the MR damper alone and thereby opening up the possibility of reducing the construction volume of the MR damper

Cyber Forensics in Cloud Computing

Cloud computing is a broad and diverse phenomenon; much of the growth represents a transfer of traditional IT services to a new cloud model. Cloud computing is anticipated to be one of the most transformative technologies in the history of computing. Cloud organizations, including the providers and customers of cloud services, have yet to establish a well-defined forensic capability. Without this they are unable to ensure the robustness and suitability of their services to support investigations of criminal activity. In this paper, we take the first steps towards defining the new area of cloud forensics, and analyze its challenges and opportunities. Keywords: Cloud Computing, Software as a Service, Platform as a Service, Infrastructure as a Service, Signature-based Analysis, Behavior-based Analysis, Cloud Forensics

Data-Driven Thermal Anomaly Detection in Large Battery Packs

The early detection and tracing of anomalous operations in battery packs are critical to improving performance and ensuring safety. This paper presents a data-driven approach for online anomaly detection in battery packs that uses real-time voltage and temperature data from multiple Li-ion battery cells. Mean-based residuals are generated for cell groups and evaluated using Principal Component Analysis. The evaluated residuals are then thresholded using a cumulative sum control chart to detect anomalies. The mild external short circuits associated with cell balancing are detected in the voltage signals and necessitate voltage retraining after balancing. Temperature residuals prove to be critical, enabling anomaly detection of module balancing events within 14 min that are unobservable from the voltage residuals. Statistical testing of the proposed approach is performed on the experimental data from a battery electric locomotive injected with model-based anomalies. The proposed anomaly detection approach has a low false-positive rate and accurately detects and traces the synthetic voltage and temperature anomalies. The performance of the proposed approach compared with direct thresholding of mean-based residuals shows a 56% faster detection time, 42% fewer false negatives, and 60% fewer missed anomalies while maintaining a comparable false-positive rate

Choosing a Data Model and Query Language for Provenance

The ancestry relationships found in provenance form a directed graph. Many provenance queries require traversal of this graph. The data and query models for provenance should directly and naturally address this graph-centric nature of provenance. To that end, we set out the requirements for a provenance data and query model and discuss why the common solutions (relational, XML, RDF) fall short. A semistructured data model is more suited for handling provenance. We propose a query model based on the Lorel query language, and briefly describe how our query language PQL extends Lorel.Engineering and Applied Science

Deterministic and Probabilistic Test Generation for Binary and Ternary Quantum Circuits

It is believed that quantum computing will begin to have an impact around year 2010. Much work is done on physical realization and synthesis of quantum circuits, but nothing so far on the problem of generating tests and localization of faults for such circuits. Even fault models for quantum circuits have been not formulated yet. We propose an approach to test generation for a wide category of fault models of single and multiple faults. It uses deterministic and probabilistic tests to detect faults. A Fault Table is created that includes probabilistic information. If possible, deterministic tests are first selected, while covering faults with tests, in order to shorten the total length of the test sequence. The method is applicable to both binary and ternary quantum circuits. The system generates test sequences and adaptive trees for fault localization for small binary and ternary quantum circuits

Terrain-Informed Self-Supervised Learning: Enhancing Building Footprint Extraction from LiDAR Data with Limited Annotations

Estimating building footprint maps from geospatial data is of paramount importance in urban planning, development, disaster management, and various other applications. Deep learning methodologies have gained prominence in building segmentation maps, offering the promise of precise footprint extraction without extensive post-processing. However, these methods face challenges in generalization and label efficiency, particularly in remote sensing, where obtaining accurate labels can be both expensive and time-consuming. To address these challenges, we propose terrain-aware self-supervised learning, tailored to remote sensing, using digital elevation models from LiDAR data. We propose to learn a model to differentiate between bare Earth and superimposed structures enabling the network to implicitly learn domain-relevant features without the need for extensive pixel-level annotations. We test the effectiveness of our approach by evaluating building segmentation performance on test datasets with varying label fractions. Remarkably, with only 1% of the labels (equivalent to 25 labeled examples), our method improves over ImageNet pre-training, showing the advantage of leveraging unlabeled data for feature extraction in the domain of remote sensing. The performance improvement is more pronounced in few-shot scenarios and gradually closes the gap with ImageNet pre-training as the label fraction increases. We test on a dataset characterized by substantial distribution shifts and labeling errors to demonstrate the generalizability of our approach. When compared to other baselines, including ImageNet pretraining and more complex architectures, our approach consistently performs better, demonstrating the efficiency and effectiveness of self-supervised terrain-aware feature learning

Spontaneous urinary bladder perforation as a cause of recurrent, progressive ascites with multiorgan dysfunction syndrome

Spontaneous rupture of the urinary bladder wall is a rare complication that may lead to intraperitoneal accumulation of urine and is mistaken for ascites from other causes. This often leads to repeated and inconclusive diagnostic tests. Here, we report the case of a 60-year-old female, with a past history of cervical cancer, who presented with recurrent episodes of pain abdomen and breathlessness over 1 year period. She was hospitalized multiple times and found to have ascites. Ultrasound and computed tomography scan of the abdomen along with an ascitic fluid analysis were done at each admission, which were inconclusive as to the cause of the ascites. A diagnostic laparoscopy to rule out peritoneal metastases showed perforation of the urinary bladder wall with intraperitoneal urine leakage. Bladder wall repair was done the following which the patient recovered uneventfully