Sleeping Environment for a Baby

ME450 Capstone Design and Manufacturing Experience: Fall 2015Kids In Danger is a nonprofit organization founded to protect children by making safety improvements to children’s products and furthering safety education. Team 26 was tasked with designing a portable sleeping environment, improving upon the commonly used Graco Pack ‘n’ Play collapsible crib design. In order to avoid the dangers associated with the central “V”-shape collapsing frame design used by Graco, they designed their portable sleeping environment to include sides that fold up, outward, and down to avoid contact with a child inside of the crib. Even if the crib were to become unlocked while in use, no portion of the frame would create a pinching point or suffocation point for the infant occupant. Additionally, the frame was designed to fold up to be smaller, and at least 10 pounds lighter than the Pack n Play.  The four locking joints on the frame have two possible positions, fully assembled and fully disassembled. A fabric insert has been created using the fabric materials extracted from a Graco Pack ‘n’ Play and modified to fit onto the designed frame, covering any exposed metal from the inside. Instead of being suspended, the thin mattress is placed on top of the fabric insert, and is supported by the ground.  All hardware and fasteners have fabric coverings and must have anti loosening devices to avoid occupant laceration, tearing of the mesh walls, or potential choking hazards. The most important design features of this frame prototype are the locking system that also allows for easy and intuitive setup, and the collapsing method and direction that ensures no hardware could fall onto the child occupant during an unexpected collapse. An obvious warning label system and setup instructions must also be provided to avoid incorrect setup and user error. The frame was tested and confirmed to abide by the ASTM 406 standard for collapsible crib strength requirements.http://deepblue.lib.umich.edu/bitstream/2027.42/117343/1/ME450-F15-Project26-FinalReport.pd

Assessing the eco-efficiency of cruise tourism at the national level: Determinants, challenges, and opportunities for sustainable development

In the face of the cruise industry\u27s rapid global expansion and its significant interplay with national tourism, economic growth, and environmental conservation, a notable gap exists in comprehensively evaluating its eco-efficiency. This study aims to fill this gap by implementing a novel analytical framework, merging the undesirable slack-based measure (SBM) model with fraction regression model (FRM) analysis. This approach is designed to dissect the complex relationship between economic development, environmental impact, and resource consumption in the context of cruise tourism. The study applies this integrated methodology to data from 11 countries, spanning from 2010 to 2018. The findings reveal marked differences in eco-efficiency across nations, with Australia and Japan standing out for their exemplary practices in natural ecology management and balanced cruise ship development. On the other hand, nations like Italy and Singapore, while supportive of the cruise industry, exhibit areas for improvement in market expansion and leveraging regional advantages. The research also brings to light the dual challenge for developing countries, such as China and Brazil, in balancing economic gains from cruise tourism with environmental concerns. A significant revelation of this study is the substantial influence of a nation\u27s per capita GDP and the rigor of environmental regulations on eco-efficiency in cruise tourism. This highlights the critical role of economic stability and policy frameworks in steering the industry towards sustainability. Interestingly, factors like foreign trade, industrial structure, and R&D investments appear to have a less pronounced impact on eco-efficiency. Moreover, the study presents a structured approach to enhance sustainability in the cruise industry, categorizing countries by eco-efficiency and returns to scale. This categorization leads to tailored recommendations for each group, focusing on the unique challenges and strengths of different nations, thus promoting effective, targeted strategies for sustainable development. Overall, the study provides a comprehensive framework for evaluating and enhancing sustainability practices in this rapidly growing industry. It also outlines potential areas for future research, considering the limitations of the current approach, thereby paving the way for further exploration into the sustainable evolution of cruise tourism

LiDAR-Forest Dataset: LiDAR Point Cloud Simulation Dataset for Forestry Application

The popularity of LiDAR devices and sensor technology has gradually empowered users from autonomous driving to forest monitoring, and research on 3D LiDAR has made remarkable progress over the years. Unlike 2D images, whose focused area is visible and rich in texture information, understanding the point distribution can help companies and researchers find better ways to develop point-based 3D applications. In this work, we contribute an unreal-based LiDAR simulation tool and a 3D simulation dataset named LiDAR-Forest, which can be used by various studies to evaluate forest reconstruction, tree DBH estimation, and point cloud compression for easy visualization. The simulation is customizable in tree species, LiDAR types and scene generation, with low cost and high efficiency.Comment: 5 page

Fine-Grained Scene Graph Generation with Data Transfer

Scene graph generation (SGG) is designed to extract (subject, predicate, object) triplets in images. Recent works have made a steady progress on SGG, and provide useful tools for high-level vision and language understanding. However, due to the data distribution problems including long-tail distribution and semantic ambiguity, the predictions of current SGG models tend to collapse to several frequent but uninformative predicates (e.g., on, at), which limits practical application of these models in downstream tasks. To deal with the problems above, we propose a novel Internal and External Data Transfer (IETrans) method, which can be applied in a plug-and-play fashion and expanded to large SGG with 1,807 predicate classes. Our IETrans tries to relieve the data distribution problem by automatically creating an enhanced dataset that provides more sufficient and coherent annotations for all predicates. By training on the enhanced dataset, a Neural Motif model doubles the macro performance while maintaining competitive micro performance. The code and data are publicly available at https://github.com/waxnkw/IETrans-SGG.pytorch.Comment: ECCV 2022 (Oral

Simulation and Analysis of Fluid-Solid Coupling of Wave Impact Sandcastle Based on COMSOL

It is a complex problem to study the interaction between sand castle and flowing water, which needs to consider the complexity of seawater flow and the stress of sand castle structure. The authors use the fluid-solid coupling model to establish the connection between the fluid field and the structural mechanical field, and use the finite element analysis to complete the simulation modeling of the transient process of wave impact and sandcastle foundation deformation. This paper analyzes the stress and the first principal strain of the sand castle foundation in the direction of flow velocity when the sand castle foundation is hit by waves, as a method to judge the strength of the sand castle.The best shape: the boundary value of sand castle collapse caused by strain have been determined, so as to obtain the maximum stress that a sand castle foundation can bear before collapse, which makes it possible to use the fatigue strength calculation theory of sand castle solid to carry out the quantitative calculation of sand castle durability. At the same time, the impact of waves is abstracted as wave motion equation. Finally, the finite element analysis technology is adopted to calculate the main strain of sandcastles of different shapes under the impact of the same wave, and through the comparison of the main strain, the authors get the sandcastle shape with the strongest anti-wave impact ability, which is the eccentric circular platform body.Affected by rain: the authors considered the effect of rainwater infiltration on the sandcastle's stress, and simplified the process of rain as a continuous and uniform infiltration of rain into the sandcastle's surface. The rain changes the gravity of the sand on the castle's surface. Simulation analysis is adopted to calculate the surface stress of sand castle with different degree of water seepage and different geometry. By comparison, it has been found that the smooth cone is more able to withstand the infiltration of rain without collapse.

Amplifying the Music Listening Experience through Song Comments on Music Streaming Platforms

Music streaming services are increasingly popular among younger generations who seek social experiences through personal expression and sharing of subjective feelings in comments. However, such emotional aspects are often ignored by current platforms, which affects the listeners' ability to find music that triggers specific personal feelings. To address this gap, this study proposes a novel approach that leverages deep learning methods to capture contextual keywords, sentiments, and induced mechanisms from song comments. The study augments a current music app with two features, including the presentation of tags that best represent song comments and a novel map metaphor that reorganizes song comments based on chronological order, content, and sentiment. The effectiveness of the proposed approach is validated through a usage scenario and a user study that demonstrate its capability to improve the user experience of exploring songs and browsing comments of interest. This study contributes to the advancement of music streaming services by providing a more personalized and emotionally rich music experience for younger generations.Comment: In the Proceedings of ChinaVis 202

Flames: Benchmarking Value Alignment of Chinese Large Language Models

The widespread adoption of large language models (LLMs) across various regions underscores the urgent need to evaluate their alignment with human values. Current benchmarks, however, fall short of effectively uncovering safety vulnerabilities in LLMs. Despite numerous models achieving high scores and 'topping the chart' in these evaluations, there is still a significant gap in LLMs' deeper alignment with human values and achieving genuine harmlessness. To this end, this paper proposes the first highly adversarial benchmark named Flames, consisting of 2,251 manually crafted prompts, ~18.7K model responses with fine-grained annotations, and a specified scorer. Our framework encompasses both common harmlessness principles, such as fairness, safety, legality, and data protection, and a unique morality dimension that integrates specific Chinese values such as harmony. Based on the framework, we carefully design adversarial prompts that incorporate complex scenarios and jailbreaking methods, mostly with implicit malice. By prompting mainstream LLMs with such adversarially constructed prompts, we obtain model responses, which are then rigorously annotated for evaluation. Our findings indicate that all the evaluated LLMs demonstrate relatively poor performance on Flames, particularly in the safety and fairness dimensions. Claude emerges as the best-performing model overall, but with its harmless rate being only 63.08% while GPT-4 only scores 39.04%. The complexity of Flames has far exceeded existing benchmarks, setting a new challenge for contemporary LLMs and highlighting the need for further alignment of LLMs. To efficiently evaluate new models on the benchmark, we develop a specified scorer capable of scoring LLMs across multiple dimensions, achieving an accuracy of 77.4%. The Flames Benchmark is publicly available on https://github.com/AIFlames/Flames

Reinforcement learning based adaptive handover in ultra-dense cellular networks with small cells

The dense deployment of the small base station (BS) in fifth-generation commination system can satisfy the user demand on high data rate transmission. On the other hand, such a scenario also increases the complexity of mobility management. In this paper, we developed a Q-learning framework exploiting user radio condition, that is, reference signal receiving power (RSRP), signal to inference and noise ratio (SINR) and transmission distance to learn the optimal policy for handover triggering. The objective of the proposed approach is to increase the mobility robustness of user in ultra-dense networks (UDNs) by minimizing redundant handover and handover failure ratio. Simulation results show that our proposed triggering mechanism efficiency suppresses ping-pong handover effect while maintaining handover failure at an acceptable level. Besides, the proposed triggering mechanism can trigger the handover process directly without HOM and TTT. The respond speed of triggering mechanism can thus be increased

Intelligent handover triggering mechanism in 5G ultra-dense networks via clustering-based reinforcement learning

Ultra-dense networks (UDNs) are considered as key 5G technologies. They provide mobile users a high transmission rate and efficient radio resource management. However, UDNs lead to the dense deployment of small base stations (BSs) that can cause stronger interference and subsequently increase the handover management complexity. At present, the conventional handover triggering mechanism of user equipment (UE) is only designed for macro mobility and thus could result in negative effects such as frequent handovers, ping-pong handovers, and handover failures on the handover process of UE at UDNs. These effects degrade the overall network performance. In addition, a massive number of BSs significantly increase the network maintenance system workload. To address these issues, this paper proposes an intelligent handover triggering mechanism for UE based on Q-learning frameworks and subtractive clustering techniques. The input metrics are first converted to state vectors by subtractive clustering, which can improve the efficiency and effectiveness of the training process. Afterward, the Q-learning framework learns the optimal handover triggering policy from the environment. The trained Q table is deployed to UE to trigger the handover process. The simulation results demonstrate that the proposed method can ensure the stronger mobility robustness of UE that is improved by 60%–90% compared to the conventional approach with respect to the number of handovers, ping-ping handover rate, and handover failure rate while maintaining other key performance indicators (KPIs), that is, a relatively high level of throughput and network latency. In addition, through integration with subtractive clustering, the proposed mechanism is further improved by an average of 20% in terms of all the evaluated KPIs