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Enhanced Terrain-Referenced Navigation Through Adaptive Radar Altimeter Error Estimation with Monte Carlo Sampling
No full textTerrain-referenced navigation serves as a reliable backup system for military aviation, but its performance is often compromised by inaccurate radar altimeter noise estimation. The wide beamwidth of radar altimeters causes complex error characteristics that vary with flight conditions and radar-terrain interactions. A mismatch between assumed and actual measurement error levels can lead to degraded navigation accuracy and potential filter divergence. This issue is exacerbated in mission scenarios such as low-altitude penetrations that require frequent rolling and pitching maneuvers. This study proposes a novel radar altimeter error modeling approach that uses Monte Carlo sampling to estimate measurement error levels on-the-fly. By incorporating a radar-terrain interaction measurement model, the method provides tighter error bounds to an extended Kalman filter, improving its response to state- and terrain-dependent error characteristics. Simulation across four distinct scenarios show that the proposed approach reduces the average divergence rate from 2.75 to 0.25%, and decreases horizontal position error by 32.3% in RMSE and 31.7% in CEP.
Alternative Stewart Platform Configuration for the Enhanced Isolation of CMG-Induced Microvibration
No full textFor sensitive payloads in satellites, microvibration is a cause of significant performance degradation. The Control Moment Gyro (CMG), an attitude control device for agile satellites, generates microvibration due to imbalance and irregularities in its high-speed flywheel. As the satellite body transmits microvibration to the sensitive payloads due to its low damping characteristics, it is imperative to isolate microvibration at the source. In this study, a hexapod microvibration isolator based on the Stewart platform is proposed with an alternative configuration. This alternative configuration, originally proposed to reduce coupling transmissibility, is adopted to provide stable performance across a broad frequency range with less weight compared to the widely used cubic configuration in Stewart platform microvibration applications. Modal analysis showed that the alternative configuration is stiffer compared to the cubic configuration. The performance of the isolator was experimentally tested with a high-torque CMG. The proposed hexapod microvibration isolator with an alternative configuration successfully isolated microvibration from the CMG without severe performance degradation.
Government expenditure and entrepreneurial activity: considering the types of entrepreneurial motivations
No full textAs a cross-national study, we analyze the relationship between the functional-level government expenditure and the different types of entrepreneurship, that is, whether it is an opportunity- or necessity-based entrepreneurship. Our analysis of 17 Organization for Economic Cooperation and Development (OECD) nations revealed that government investments in economic affairs are positively related to opportunity-driven entrepreneurship, but negatively to necessity-driven entrepreneurship. On the other hand, government spending on education is positively related to necessity-driven entrepreneurship. Further, we find that larger government expenditure on education in Asian countries encourages more entrepreneurs to start their businesses than the rest of the OECD.
Multichannel Advertising: Budget Allocation in the Presence of Spillover and Carryover Effects05
No full textProblem definition: This paper explores budget allocation strategies for a multichannel ad campaign, where a marketing agency strives to maximize the total conversions by dynamically adjusting budget allocation over marketing channels. A salient feature of the problem is the interplay of spillover and carryover effects; namely, customers are exposed to ads through multiple channels, and thus ads from one channel affect the effectiveness of the subsequent ads from other channels. Methodology/results: We construct a simple model that captures the essential features of this problem. Our theoretical analysis yields two main insights. First, motivated by common practice based on the last-click attribution method, we examine a class of budget allocation policies that are oblivious to the spillover and carryover effects. If the agency decreases the budget on a channel based on past low conversions while neglecting to account for the fact that the ads from that channel induced conversions through other channels, then the conversions from that channel will decrease. Consequently, the agency will further decrease the budget on the channel. This pattern repeats, eventually leading to suboptimal performance in the long run. Second, we derive a fluid approximation to consumer dynamics across multiple channels, which lends itself to characterizing structural properties of optimal dynamic budget allocation policies that internalize the cross-channel interactions. To enable practical implementation, we propose a static budget allocation policy that is both tractable in practice and near optimal for long campaigns. Managerial implications: Our theoretical results provide normative guidance for budget allocation in multichannel ad campaigns. We illustrate the efficacy of our proposed method through a numerical study based on data from an online multichannel ad campaign.
Control-Resilient Roller Wear Prediction for Thin Wire Flattening Process via an Internal Sound-Guided Dynamic Conditional Network
No full textAs modern manufacturing systems increasingly employ adaptive control mechanisms, there is a need for monitoring solutions that can operate robustly across varying process conditions. In particular, the thin wire flattening process requires a control-resilient model to predict roller wear-a critical factor affecting wire quality even-as roller rotation conditions are adjusted in real-time via feedback control. To minimize roller waste and maintain product quality, precise and robust monitoring of roller wear is essential. This study proposes a dynamic conditional convolutional network (DCCN) for resilient roller wear prediction, utilizing sound data from the internal sound sensors (ISSs) and operational data from machine controllers. The DCCN dynamically adjusts its internal parameters by modulating the weights in its dynamic conditional layer based on real-time machine operational data. This mechanism enables the model to respond adaptively to changes in roller rotation conditions, allowing it to maintain high predictive accuracy across diverse operational settings. The DCCN demonstrated robust performance with 93.54% accuracy under previously unseen operational conditions, compared to only 53.42% and 73.43% accuracy achieved by conventional Convolutional Neural Network (CNN) and multimodal network models, respectively. Furthermore, layer-wise feature visualization illustrated the model's capability to provide control-invariant predictions, making it well-suited for deployment in adaptive, sustainable manufacturing systems.
Pan-reactome analysis of Streptomyces strains reveals association and disconnection between primary and secondary metabolism
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Direct evidence of a major merger in the Perseus cluster
No full textAlthough the Perseus cluster has often been regarded as an archetypical relaxed galaxy cluster, several lines of evidence, including ancient, large-scale cold fronts, asymmetric plasma morphology, filamentary galaxy distribution and so on, provide a conflicting view of its dynamical state, suggesting that the cluster might have experienced a major merger. However, the absence of a clear merging companion identified so far hampers our understanding of the evolutionary track of the Perseus cluster consistent with these observational features. Here, through careful weak-lensing analysis, we successfully identified the missing subcluster halo (total mass M-200=1.70(-0.59)(+0.73)x10(14)M(circle dot) at the >5 sigma level centred on NGC 1264, which is located similar to 430 kpc west of the Perseus main cluster core. Moreover, a significant (>3 sigma) mass bridge, which is also traced by the cluster member galaxies, is detected between the Perseus main and subclusters, which serves as direct evidence of gravitational interaction. With idealized numerical simulations, we demonstrate that an similar to 3:1 off-axis major merger can create the cold front observed similar to 700 kpc east of the main cluster core and generate the observed mass bridge through multiple core crossings. This discovery resolves the long-standing puzzle of Perseus's dynamical state.
Musical Word Embedding for Music Tagging and Retrieval
No full textWord embedding has become an essential means for text-based information retrieval. Typically, word embeddings are learned from large quantities of general and unstructured text data. However, in the domain of music, word embedding may have difficulty understanding musical contexts or recognizing music-related entities like artists and tracks. To address this issue, we propose a new approach called Musical Word Embedding (MWE), which involves learning from various types of texts, including both everyday and music-related vocabulary. We integrate MWE into an audio-word joint representation framework for tagging and retrieving music, using words like tag, artist, and track that have different levels of musical specificity. Through extensive experiments, we demonstrate that the effectiveness of musical supervision varies by task - specifically, tag-level supervision improves tagging performance while track-level supervision enhances retrieval performance. This finding suggests that the choice of musical supervision in representation learning needs to be carefully considered based on the target task. To balance this compromise, we suggest multi-prototype training that uses words with different levels of musical specificity jointly. We evaluate both word embedding and audio-word joint embedding on four tasks (tag rank prediction, music tagging, query-by-tag, and query-by-track) across two datasets (Million Song Dataset and MTG-Jamendo). The results show that the suggested MWE is more efficient and effective for both in-domain and out-of-domain datasets compared to conventional word embedding.