Search Results (14)

Background: In recent years, advancements in digital dentistry have provided new opportunities for more predictable and efficient treatment options, particularly in patients with failing dentition. This study aimed to evaluate the effectiveness and accuracy of a fully digital workflow using stackable surgical templates for complete dental arch rehabilitation with implant-supported fixed restorations. Methods: Four patients, comprising two males and two females with a mean age of 66 years, were included in this case series. Each patient underwent meticulous digital planning, including CBCT and intraoral scanning, to create a virtual patient for preoperative assessment and virtual treatment planning. The assessment of the trueness of implant positioning was conducted in Geomagic Control X software (version 2017.0.3) by referencing anatomical landmarks from both the preoperative and one-year postoperative CBCT scans. Results: A total of 25 dental implants were placed in the maxilla, followed by the installation of long-term provisional restorations. The results showed minimal deviation between the planned and actual implant positions, with mean 3D coronal, apical, and angular discrepancies of 0.87 mm, 2.04 mm, and 2.67°, respectively. All implants achieved successful osseointegration, and no failures were recorded, resulting in a 100% survival rate at the one-year follow-up. Patients reported high satisfaction with both the esthetic and functional outcomes based on their subjective feedback. Conclusions: The findings suggest that the use of a fully digital workflow with stackable surgical templates is a reliable and effective approach for immediate implant placement and prosthetic rehabilitation, enhancing treatment precision and patient comfort. Full article

This study demonstrates a failure identification methodology applied to a preprocessing facility generating conversion-ready feedstocks from biomass meeting conversion process critical quality attribute (CQA) specifications. Failure Modes and Effects Analysis (FMEA) was used as an industrially relevant risk analysis approach to evaluate a logging residue preprocessing system to prepare feedstock for pyrolysis conversion. Risk evaluations considered both system-level and operation unit-level assessments considering process efficiency, product quality, cost, sustainability, and safety. Key outputs included estimations of semi-quantitative risk scores for each failure, identification of the failure impacts, identification of failure causes associated with material attributes and process parameters, ranking success rates of failure detection methods, and speculation of potential mitigation strategies for decreasing failure risk scores. Results showed that deviations from moisture specifications had cascading consequences for other CQAs along with process safety implications. Failures linked to fixed carbon specifications carried the highest risk scores for product quality and process efficiency impacts. As increased throughput can be inversely related to meeting product quality specifications; achieving throughput and other material-based CQAs simultaneously will likely require system optimization or prioritization based on system economics. Ultimately, this work successfully demonstrates FMEA as a risk analysis approach for other bioenergy process systems. Full article

Integer Aperture Bootstrapping (IAB) is a crucial method for testing ambiguity acceptance in carrier-phase differential global navigation satellite system (CDGNSS) positioning. It has the advantage that integrity parameters, such as the failure rate, can be analytically calculated, which is essential in safety-of-life applications. Although the IAB methods have been extensively studied, their threshold-determining method is still not well explained, theoretically. In this study, a new method, named Analytical Integer Aperture Bootstrapping (AIAB), is theoretically derived to determine the optimal IAB threshold. AIAB is novel in that: (1) a precise and easy-to-compute expression has been developed to describe the relationship between the IAB threshold and the failure rate, (2) an analytical function model has been derived from the expression to determine the IAB threshold; moreover, the function model is simplified, and (3) a data-constraint approach has been proposed to reduce the complexity of IAB. In the global CDGNSS simulations, AIAB is shown to outperform the existing IAB methods under both strong and weak models, particularly at low fix rates, which are 23% to 40% higher than the basic IAB method. The Monte Carlo simulation results show that AIAB can obtain almost theoretically the same performance as Optimal Integer Aperture (OIA). Full article

In various industrial domains, machinery plays a pivotal role, with bearing failure standing out as the most prevalent cause of malfunction, contributing to approximately 41% to 44% of all operational breakdowns. To address this issue, this research employs a lightweight neural network, boasting a mere 8.69 K parameters, tailored for implementation on an FPGA (field-programmable gate array). By integrating an incremental network quantization approach and fixed-point operation techniques, substantial memory savings amounting to 63.49% are realized compared to conventional 32-bit floating-point operations. Moreover, when executed on an FPGA, this work facilitates real-time bearing condition detection at an impressive rate of 48,000 samples per second while operating on a minimal power budget of just 342 mW. Remarkably, this system achieves an accuracy level of 95.12%, showcasing its effectiveness in predictive maintenance and the prevention of costly machinery failures. Full article

Task scheduling poses a wide variety of challenges in the cloud computing paradigm, as heterogeneous tasks from a variety of resources come onto cloud platforms. The most important challenge in this paradigm is to avoid single points of failure, as tasks of various users are running at the cloud provider, and it is very important to improve fault tolerance and maintain negligible downtime in order to render services to a wide range of customers around the world. In this paper, to tackle this challenge, we precisely calculated priorities of tasks for virtual machines (VMs) based on unit electricity cost and these priorities are fed to the scheduler. This scheduler is modeled using a deep reinforcement learning technique which is known as the DQN model to make decisions and generate schedules optimally for VMs based on priorities fed to the scheduler. This research is extensively conducted on Cloudsim. In this research, a real-time dataset known as Google Cloud Jobs is used and is given as input to the algorithm. This research is carried out in two phases by categorizing the dataset as a regular or large dataset with real-time tasks with fixed and varied VMs in both datasets. Our proposed DRFTSA is compared to existing state-of-the-art approaches, i.e., PSO, ACO, and GA algorithms, and results reveal that the proposed DRFTSA minimizes makespan compared to PSO, GA, and ACO by 30.97%, 35.1%, and 37.12%, rates of failure by 39.4%, 44.13%, and 46.19%, and energy consumption by 18.81%, 23.07%, and 28.8%, respectively, for both regular and large datasets for both fixed and varied VMs. Full article

Wheel-based network resilience passive optical network (PON) based on mode division multiplexing (MDM) can be integrated with optical code division multiple access (OCDMA) schemes efficiently for the fixed and backhaul traffic under normal and break/failure fiber operating conditions. In this work, a bidirectional 10/2.5 Gbit/s hybrid MDM-OCDMA-PON system using multi-weight zero cross-correlation (MWZCC) code is proposed. Donut modes 0 and 1 are incorporated by the MDM technique in the proposed system. The benefit of this work is to offer an inexpensive, high-bandwidth and advanced long-haul network with satisfactory resource utilization ability for fiber links with protection against faults and to improve the reliability along with survivability of the network. The simulation results show the successful realization of the multimode fiber (MMF) link at 1.6 km in the uplink and 1.2 km in the downlink directions under an acceptable bit error rate (BER). The minimum accepted received power of −31 dBm in uplink and −27 dBm in downlink over 1 km link at 10/2.5 Gbit/s rate is obtained. Moreover, the minimum received power of −20 dBm in uplink and −30 dBm downlink is achieved by using MWZCC code compared to other codes handling 58 simultaneous end users. Further, the influence of fiber impairments and connected devices on the proposed approach is numerically evaluated. Moreover, it is shown that the wheel based proposed approach performs well than other topologies for the bidirectional network resilience transmission. Full article

Current speech recognition systems with fixed vocabularies have difficulties recognizing Out-of-Vocabulary words (OOVs) such as proper nouns and new words. This leads to misunderstandings or even failures in dialog systems. Ensuring effective speech recognition is crucial for the proper functioning of robot assistants. Non-native accents, new vocabulary, and aging voices can cause malfunctions in a speech recognition system. If this task is not executed correctly, the assistant robot will inevitably produce false or random responses. In this paper, we used a statistical approach based on distance algorithms to improve OOV correction. We developed a post-processing algorithm to be combined with a speech recognition model. In this sense, we compared two distance algorithms: Damerau–Levenshtein and Levenshtein distance. We validated the performance of the two distance algorithms in conjunction with five off-the-shelf speech recognition models. Damerau–Levenshtein, as compared to the Levenshtein distance algorithm, succeeded in minimizing the Word Error Rate (WER) when using the MoroccanFrench test set with five speech recognition systems, namely VOSK API, Google API, Wav2vec2.0, SpeechBrain, and Quartznet pre-trained models. Our post-processing method works regardless of the architecture of the speech recognizer, and its results on our MoroccanFrench test set outperformed the five chosen off-the-shelf speech recognizer systems. Full article

In this study, a scheme for leak localization on a cylinder tank bottom using acoustic emission (AE) is proposed. This approach provides a means of early failure detection, thus reducing financial damage and hazards to the environment and users. The scheme starts with the hit detection process using a constant false alarm rate (CFAR) and a fixed thresholding method for a time of arrival (TOA) and an end-time determination. The detected hits are then investigated to group those originating from the same AE source together by enforcing an event definition and a similarity score. Afterwards, these newly grouped hits are processed by a time difference of arrival (TDOA) to find the locations of the events. Since the locations of the events alone do not pinpoint the leak location, a data density analysis using a Voronoi diagram is employed to find the area with the highest possibility of a leak’s existence. The proposed method was validated using the Hsu-Nielsen test on a cylinder tank bottom under a one-failed-sensor scenario, which returned a highly accurate result across multiple test locations. Full article

To integrate unmanned aerial vehicles (UAVs) into the national airspace in a safe manner, a risk-based approach to the regulation of UAVs is adopted in many countries. Thus, the capacity to permit UAVs in urban airspace also needs to be evaluated in a risk-based sense. In this regard, this paper proposes a methodology to analyze the capacity of UAV corridors on the basis of third-party risk on the ground. By linking the collision rate of the corridor and the failure rates of UAVs with the number of fatalities on the ground, the capacity of the UAV corridor is derived to satisfy the target level of safety. To model the collision rate of UAVs in the corridor, the Reich collision risk model is utilized. Moreover, a ground risk map is generated to compute the third-party risk on the ground using the databases for Seoul, Korea. The results show that the failure rate of UAVs is the dominant factor for determining the capacity of the corridor, even if the number of corridors increases. The proposed methodology could be useful to manage the number of flights for applications where the UAV corridor is fixed and flight continues, such as package delivery. Full article

This research presents an analysis and management strategy for hovering hexacopter with one or more failing motors. Of late, multirotor drones have become particularly popular, and all drones have been increasing in popularity. Unlike a fixed-wing drone, failure of motors in a multirotor craft may cause safety problems. Numerous published articles have proposed solving this problem by redesigning the control law or control gain. This approach, however, is difficult to implement because change of control gain usually involves connecting external devices. This paper proposes to keep the control gain unchanged but reallocate the thrusts. Simulations are conducted on a hexacopter in various hovering modes. Some hovering state problems are investigated for the linearized dynamics but also numerically verified for the original nonlinear dynamics. In case some motors of a hexacopter fail in flight, an allocation matrix is proposed to redistribute required thrusts to functional motors. Seven cases of motor failure are studied. This paper analytically proves that limited controllability for emergency landing is feasible in four scenarios at the linear level, but the other three scenarios are completely uncontrollable. Numerical simulations are presented to demonstrate the validity of our algorithm. An online video of real flight also confirms our results. This paper potentially helps the design of failure management of rotors and increases the successful rate of emergent landing. Full article

Monitoring of respiration and body movements during sleep is a part of screening sleep disorders related to health status. Nowadays, thermal-based methods are presented to monitor the sleeping person without any sensors attached to the body to protect privacy. A non-contact respiration monitoring based on thermal videos requires visible facial landmarks like nostril and mouth. The limitation of these techniques is the failure of face detection while sleeping with a fixed camera position. This study presents the non-contact respiration monitoring approach that does not require facial landmark visibility under the natural sleep environment, which implies an uncontrolled sleep posture, darkness, and subjects covered with a blanket. The automatic region of interest (ROI) extraction by temperature detection and breathing motion detection is based on image processing integrated to obtain the respiration signals. A signal processing technique was used to estimate respiration and body movements information from a sequence of thermal video. The proposed approach has been tested on 16 volunteers, for which video recordings were carried out by themselves. The participants were also asked to wear the Go Direct respiratory belt for capturing reference data. The result revealed that our proposed measuring respiratory rate obtains root mean square error (RMSE) of $1.82\pm 0.75$ bpm. The advantage of this approach lies in its simplicity and accessibility to serve users who require monitoring the respiration during sleep without direct contact by themselves. Full article

In this paper, a versatile Markovian queueing system is considered. Given a fixed threshold level c, the server serves customers one a time when the queue length is less than c, and in batches of fixed size c when the queue length is greater than or equal to c. The server is subject to failure when serving either a single or a batch of customers. Service rates, failure rates, and repair rates, depend on whether the server is serving a single customer or a batch of customers. While the analytical method provides the initial probability vector, we use the entropy principle to obtain both the initial probability vector (for comparison) and the tail probability vector. The comparison shows the results obtained analytically and approximately are in good agreement, especially when the first two moments are used in the entropy approach. Full article

Global navigation satellite system (GNSS) integer ambiguity acceptance test is one of the open problems in GNSS data processing. A number of ambiguity acceptance tests have been proposed from different perspectives and then unified into the integer aperture estimation framework. The existing comparative studies indicate that the impact of test statistics form on the test performance is less critical, while how to construct an efficient, practical test threshold is still challenging. Based on the likelihood ratio test theory, a new computationally efficient ambiguity acceptance test with controllable success fix rate, namely the fixed likelihood ratio (FL-) approach is proposed, which does not require Monte Carlo simulation. The study indicates that the fixed failure rate (FF-) approach can only control the overall failure rate of the acceptance region, but the local failure rate is not controllable. The proposed FL-approach only accepts the fixed solution meeting the likelihood ratio requirement. With properly chosen likelihood ratio threshold, the FL-approach achieves comparable success rate as the FF-approach and even lower failure rate than the FF-approach for the strong underlying model cases. The fixed success fix rate of the FL-approach is verified with both simulation data and real GNSS data. The numerical results indicate that the success fix rate of the FL-approach achieves >98% while the failure rate is <1.5%. The real-time kinematic (RTK) positioning with ambiguities tested by the FL-approach achieved 1–2cm horizontal precision and 2–4 cm vertical precision for all tested baselines, which confirms that the FL-approach can serve as a reliable and efficient threshold determination method for the GNSS ambiguity acceptance test problem. Full article

The reliability of cutting tools is critical to machining precision and production efficiency. The conventional statistic-based reliability assessment method aims at providing a general and overall estimation of reliability for a large population of identical units under given and fixed conditions. However, it has limited effectiveness in depicting the operational characteristics of a cutting tool. To overcome this limitation, this paper proposes an approach to assess the operation reliability of cutting tools. A proportional covariate model is introduced to construct the relationship between operation reliability and condition monitoring information. The wavelet packet transform and an improved distance evaluation technique are used to extract sensitive features from vibration signals, and a covariate function is constructed based on the proportional covariate model. Ultimately, the failure rate function of the cutting tool being assessed is calculated using the baseline covariate function obtained from a small sample of historical data. Experimental results and a comparative study show that the proposed method is effective for assessing the operation reliability of cutting tools. Full article