Rescue Target Research Articles

DLSW-YOLOv8n: A Novel Small Maritime Search and Rescue Object Detection Framework for UAV Images with Deformable Large Kernel Net

Unmanned aerial vehicle maritime search and rescue target detection is susceptible to external factors, which can seriously reduce detection accuracy. To address these challenges, the DLSW-YOLOv8n algorithm is proposed combining Deformable Large Kernel Net (DL-Net), SPD-Conv, and WIOU. Firstly, to refine the contextual understanding ability of the model, the DL-Net is integrated into the C2f module of the backbone network. Secondly, to enhance the small target characterization representation, a spatial-depth layer is used instead of pooling in the convolution module, and an additional detection head is integrated into the low-level feature map. The loss function is improved to enhance small target localization performance. Finally, a UAV maritime target detection dataset is employed to demonstrate the effectiveness of the proposed algorithm, whose results show that DLSW-YOLOv8n achieves a detection accuracy of 79.5%, which represents an improvement of 13.1% compared to YOLOv8n.

Loss of Efficacy in Ventral Intermediate Nucleus Stimulation for Essential Tremor

The primary aim of this study is to report long-term outcomes associated with deep brain stimulation (DBS) of the ventral intermediate nucleus (VIM) performed at our institution. We further aimed to elicit the factors associated with loss of efficacy and to discuss the need for exploring and establishing reliable rescue targets. To study long-term outcomes, we performed a retrospective chart review and extracted tremor scores of 43 patients who underwent VIM DBS lead implantation for essential tremor at our center. We further evaluated factors that could influence outcomes over time, including demographics, body mass index, duration of follow-up, degree of parenchymal atrophy indexed by the global cortical atrophy scale, and third ventricular width. In this cohort, tremor scores on the latest follow-up (median 52.7months) were noted to be worse than initial postoperative scores in 56% of DBS leads. Furthermore, 14% of leads were associated with clinically significant loss of benefit. Factors including the length of time since the lead implantation, age at the time of surgery, sex, body mass index, preoperative atrophy, and third ventricular width were not predictive of long-term outcomes. Our study identified a substantial subgroup of VIM-DBS patient who experienced a gradual decline in treatment efficacy over time. We propose that this phenomenon can be attributed primarily to habituation and disease progression. Furthermore, we discuss the need to establish reliable and effective rescue targets for this subpopulation of patients, with ventral-oralis complex and dentate nucleus emerging as potential candidates.

Underwater Rescue Target Detection Based on Acoustic Images.

In order to effectively respond to floods and water emergencies that result in the drowning of missing persons, timely and effective search and rescue is a very critical step in underwater rescue. Due to the complex underwater environment and low visibility, unmanned underwater vehicles (UUVs) with sonar are more efficient than traditional manual search and rescue methods to conduct active searches using deep learning algorithms. In this paper, we constructed a sound-based rescue target dataset that encompasses both the source and target domains using deep transfer learning techniques. For the underwater acoustic rescue target detection of small targets, which lack image feature accuracy, this paper proposes a two-branch convolution module and improves the YOLOv5s algorithm model to design an acoustic rescue small target detection algorithm model. For an underwater rescue target dataset based on acoustic images with a small sample acoustic dataset, a direct fine-tuning using optical image pre-training lacks cross-domain adaptability due to the different statistical properties of optical and acoustic images. This paper therefore proposes a heterogeneous information hierarchical migration learning method. For the false detection of acoustic rescue targets in a complex underwater background, the network layer is frozen during the hierarchical migration of heterogeneous information to improve the detection accuracy. In addition, in order to be more applicable to the embedded devices carried by underwater UAVs, an underwater acoustic rescue target detection algorithm based on ShuffleNetv2 is proposed to improve the two-branch convolutional module and the backbone network of YOLOv5s algorithm, and to create a lightweight model based on hierarchical migration of heterogeneous information. Through extensive comparative experiments conducted on various acoustic images, we have thoroughly validated the feasibility and effectiveness of our method. Our approach has demonstrated state-of-the-art performance in underwater search and rescue target detection tasks.

Research on Improved VGG-16 Model Based on Transfer Learning for Acoustic Image Recognition of Underwater Search and Rescue Targets

Research on Improved VGG-16 Model Based on Transfer Learning for Acoustic Image Recognition of Underwater Search and Rescue Targets

YOLOv7-CSAW for maritime target detection.

The issue of low detection rates and high false negative rates in maritime search and rescue operations has been a critical problem in current target detection algorithms. This is mainly due to the complex maritime environment and the small size of most targets. These challenges affect the algorithms' robustness and generalization. We proposed YOLOv7-CSAW, an improved maritime search and rescue target detection algorithm based on YOLOv7. We used the K-means++ algorithm for the optimal size determination of prior anchor boxes, ensuring an accurate match with actual objects. The C2f module was incorporated for a lightweight model capable of obtaining richer gradient flow information. The model's perception of small target features was increased with the non-parameter simple attention module (SimAM). We further upgraded the feature fusion network to an adaptive feature fusion network (ASFF) to address the lack of high-level semantic features in small targets. Lastly, we implemented the wise intersection over union (WIoU) loss function to tackle large positioning errors and missed detections. Our algorithm was extensively tested on a maritime search and rescue dataset with YOLOv7 as the baseline model. We observed a significant improvement in the detection performance compared to traditional deep learning algorithms, with a mean average precision (mAP) improvement of 10.73% over the baseline model. YOLOv7-CSAW significantly enhances the accuracy and robustness of small target detection in complex scenes. This algorithm effectively addresses the common issues experienced in maritime search and rescue operations, specifically improving the detection rates and reducing false negatives, proving to be a superior alternative to current target detection algorithms.

Object Detection through Fires Using Violet Illumination Coupled with Deep Learning

Fire accidents threaten public safety. One of the greatest challenges during fire rescue is that firefighters need to find objects as quickly as possible in an environment with strong flame luminosity and dense smoke. This paper reports an optical method, called violet illumination, coupled with deep learning, to significantly increase the effectiveness in searching for and identifying rescue targets during a fire. With a relatively simple optical system, broadband flame luminosity can be spectrally filtered out from the scattering signal of the object. The application of deep learning algorithms can further and significantly enhance the effectiveness of object search and identification. The work shows that this novel optics–deep learning combined method can improve the object identification accuracy from 7.0% with the naked eye to 83.1%. A processing speed of 10 frames per second can also be achieved on a single CPU. These results indicate that the optical method coupled with machine learning algorithms can potentially be a very useful technique for object searching in fire rescue, especially considering the emergence of low-cost, powerful, compact violet light sources and the rapid development of machine learning methods. Potential designs for practical systems are also discussed.

Abstract 6157: USP7 is a promising thersapeutic target for neuroblastoma

Abstract Background: The ubiquitin-proteasome system (UPS) plays an essential role in post-translational modification of proteins to maintain proteostasis. Ubiquitination of target protein is coordinated by different enzymes, which add and link ubiquitin to each other in specific ways that determine the fate of the protein for to be degraded or to be included into protein complex to induce signaling pathways. Ubiquitination is further regulated by deubiquitinases, which remove ubiquitin and rescue target protein from being degraded or diverted from it signaling role. UPS dysregulation has been linked to many human diseases, including cancer. Ubiquitin-specific protease 7 (USP7) is a deubiquitinase that plays a critical role in immune response, tumor suppression and DNA repair. USP7 overexpression has been associated with tumor aggressiveness in a variety of tumors, including in neuroblastoma (NB). Therefore, USP7 is a potential therapeutic target for NB. Some new specific USP7 inhibitors are highly potent and selective and have demonstrated significant antitumor activity in preclinical models of adult cancer. We hypothesize that inhibition of USP7 alone or in combination with epigenetic drugs like HDAC inhibitors will be more effective against NB tumor growth. To evaluate the efficacy of USP7 inhibition with specific USP7 inhibitors alone or with combination with epigenetic drugs like HDAC inhibitors (Saha) or chemotherapy against NB tumor growth, NB cell lines were treated with increasing concentrations of USP7 inhibitors alone or in combination with chemotherapy or with HDAC inhibitors. Cell proliferation was measured using continuous live cell imaging and cell viability using AlamarBlue assay. Apoptosis was measured by caspase cleavage using Caspase 3/7 apoptosis assay as well as by PARP cleavage by Western Blotting (WB). Changes in various proteins were measured by WB and co-immunoprecipitation were performed to analyze the ubiquitin status of specific protein targets regulated by USP7. Pharmacologic inhibition of USP7 resulted in significant decreases in both cell proliferation and cell viability in vitro only in p53 wild-type NB cell lines. There was no correlation seen between MYCN amplification status and treatment response. USP7 inhibition induced apoptosis as well as necroptosis with dose-dependent increase. Treatment with USP7 inhibitors led to decrease p53 ubiquitination leading increased of p53 protein expression in all sensitive cell lines tested and decreased MYCN protein level in MYCN-amplified cell lines. EZH2, which is a predominant target of USP7 is significantly downregulated with increasing dose of USP7 inhibitors. In addition, the combination of USP7 inhibitors with chemotherapy commonly used in NB or with combination with HDAC inhibitors showed enhanced efficacy. Our data suggests that USP7 inhibition may be a promising therapeutic strategy for children with high-risk and relapsed neuroblastoma. Citation Format: Christophe Le Clorennec, Karen Lee, Peter Zage. USP7 is a promising thersapeutic target for neuroblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6157.

A method for evaluating population and infrastructure exposed to natural hazards: tests and results for two recent Tonga tsunamis

BackgroundCoastal communities are highly exposed to ocean- and -related hazards but often lack an accurate population and infrastructure database. On January 15, 2022 and for many days thereafter, the Kingdom of Tonga was cut off from the rest of the world by a destructive tsunami associated with the Hunga Tonga Hunga Ha’apai volcanic eruption. This situation was made worse by COVID-19-related lockdowns and no precise idea of the magnitude and pattern of destruction incurred, confirming Tonga’s position as second out of 172 countries ranked by the World Risk Index 2018. The occurrence of such events in remote island communities highlights the need for (1) precisely knowing the distribution of buildings, and (2) evaluating what proportion of those would be vulnerable to a tsunami.Methods and ResultsA GIS-based dasymetric mapping method, previously tested in New Caledonia for assessing and calibrating population distribution at high resolution, is improved and implemented in less than a day to jointly map population clusters and critical elevation contours based on runup scenarios, and is tested against destruction patterns independently recorded in Tonga after the two recent tsunamis of 2009 and 2022. Results show that ~ 62% of the population of Tonga lives in well-defined clusters between sea level and the 15 m elevation contour. The patterns of vulnerability thus obtained for each island of the archipelago allow exposure and potential for cumulative damage to be ranked as a function of tsunami magnitude and source area.ConclusionsBy relying on low-cost tools and incomplete datasets for rapid implementation in the context of natural disasters, this approach works for all types of natural hazards, is easily transferable to other insular settings, can assist in guiding emergency rescue targets, and can help to elaborate future land-use planning priorities for disaster risk reduction purposes.

범죄피해자 구조금 산출 방법의 문제점 및 개선방안범죄피해자 구조금 산출 방법의 문제점 및 개선방안

In 2010, the ｢Crime Victims Aid Act｣ was abolished and the new ｢Crime Victims Protection Act｣ was enacted, bringing about continuous changes to the development of the crime victim aid system which is guaranteed as a basic right in the Constitution.
 In the Rescue Act, which was abolished due to this influence, the relief money paid to victims to be rescued was a fixed amount in the form of a consolation money, but in the later revised Protection Act, the concept of actual income or average income of rescue victims was introduced, a system was implemented to pay differential rescue funds according to the relationship between the rescue victim and the rescue target victim.
 Meanwhile, the amount of relief also continued to increase each year in line with the rise in average wages, and the target for disability relief was expanded from the previous 10th grade to the 14th grade.
 In addition, there have been many previous studies that the protection law needs to be revised as foreign spouse rescue and victims of incidents caused by negligence are excluded from the rescue target.
 In addition, there are several problems with the rescue system, but the first problem to be improved is the uniform calculation method of the rescue fund that does not consider the age of the rescue victim at the time of the incident.
 It has been pointed out that the relief funds received by the victims to be rescued are not fair due to these problems, the ranking of unmarried children over the age of 19 who live on the income of rescue victims is not reasonable, and it is very insufficient to guarantee their future life.
 Therefore, this study aims to suggest a plan to improve the structural fund calculation method that can fundamentally improve the problems of the structural fund system.

A detection method of the rescue targets in the marine casualty based on improved YOLOv5s.

In recent years, with the deep exploitation of marine resources and the development of maritime transportation, ship collision accidents occur frequently, which leads to the increasingly heavy task of maritime Search and Rescue (SAR). Unmanned Aerial Vehicles (UAVs) have the advantages of flexible maneuvering, robust adaptability and extensive monitoring, which have become an essential means and tool for emergency rescue of maritime accidents. However, the current UAVs-based drowning people detection technology has insufficient detection ability and low precision for small targets in high-altitude images. Moreover, limited by the load capacity, UAVs do not have enough computing power and storage space, resulting in the existing object detection algorithms based on deep learning cannot be directly deployed on UAVs. To solve the two issues mentioned above, this paper proposes a lightweight deep learning detection model based on YOLOv5s, which is used in the SAR task of drowning people of UAVs at sea. First, an extended small object detection layer is added to improve the detection effect of small objects, including the extraction of shallow features, a new feature fusion layer and one more prediction head. Then, the Ghost module and the C3Ghost module are used to replace the Conv module and the C3 module in YOLOv5s, which enable lightweight network improvements that make the model more suitable for deployment on UAVs. The experimental results indicate that the improved model can effectively identify the rescue targets in the marine casualty. Specifically, compared with the original YOLOv5s, the improved model mAP@0.5 value increased by 2.3% and the mAP@0.5:0.95 value increased by 1.1%. Meanwhile, the improved model meets the needs of the lightweight model. Specifically, compared with the original YOLOv5s, the parameters decreased by 44.9%, the model weight size compressed by 39.4%, and Floating Point Operations (FLOPs) reduced by 22.8%.

Distant object localization with a single image obtained from a smartphone in an urban environment

To expedite the initial response and ensure the safety of citizens and facilities during a disaster, it is critical to quickly obtain the information around the disaster site. In particular, identifying the location of non-stationary objects, such as vehicles, is as important as that of rescue targets because these objects may impede first responders. As the location of these non-stationary objects cannot be predicted, the most feasible solution is utilizing the mobile sensing imagery provided by citizens near the site. Several studies on object localization have used data from smartphones obtained through participatory sensing. However, most of these studies required multiple images or additional infrastructure, which is unsuitable for sudden and urgent cases. The single-image approach using depth estimation with deep learning is limited as it only estimates the relative location and does not provide the absolute geographic coordinates of interest. To this end, we developed an algorithm to estimate the absolute locations of objects from a single smartphone image. Our solution combines theories of photogrammetry, coordinate transformation, object detection, and depth estimation. First, an image and the corresponding device sensor data were acquired using a smartphone application. Second, target vectors that point to target objects from the smartphone’s perspective center are defined and constructed in a local coordinate system based on photogrammetric geometry, object detection, and depth estimation. Third, the constructed target vectors were transformed from local to geographic coordinates. Finally, the locations of the target objects are calculated and displayed in a geographic coordinate system for a quick decision-making process. The final absolute locations were compared with the ground reference points obtained from real-time kinematic positioning (RTK) using the global navigation satellite system (GNSS) and the total station (TS). 70 target objects were identified from the 18 images, and the estimated location had an average absolute location error of 4.118 m.

How to Improve the Cooperation Mechanism of Emergency Rescue and Optimize the Cooperation Strategy in China: A Tripartite Evolutionary Game Model.

To reveal the interaction and influence mechanism between emergency rescue entities, and to explore and optimize a cooperation mechanism of emergency rescue entities, a tripartite evolutionary game model of emergency rescue cooperation based on government rescue teams, social emergency organizations, and government support institutions was constructed. The stability of each game subject’s strategy choice was explored. Simulation analysis was applied to investigate the influence mechanism of key parameters on the evolution of the game subject’s strategy combination. The research results show that government rescue teams, social emergency organizations, and government support institutions have consistent political demands and rescue targets in emergency rescue cooperation. The game subjects are driving forces for each other to choose positive strategies. The game evolution process of the emergency cooperation model shows a “mobilization-coordination” feature. At the same time, the emergency capital stock formed based on trust relationships, information matching, and institutional norms between game subjects can promote the evolution of the game system toward (1,1,1). In addition, for government organizations with limited emergency resources, the average allocation of emergency resources is not the optimal solution for emergency rescue efficiency. However, it is easier to achieve the overall target of emergency rescue cooperation by investing limited emergency resources in key variables that match the on-site situation. On this basis, combined with the practice of emergency rescues in emergencies, countermeasures and solutions are proposed to optimize the mechanism and improve the efficiency of emergency rescue cooperation.

Path-Based Sensors: Paths as Sensors, Bayesian Updates, and Shannon Information Gathering

Consider a sensor that reports whether or not an event has occurred somewhere along a path, but that has no conception of where along the path that event has occurred. We name this type of sensor a path-based sensor and describe the recursive Bayesian update that can be used to calculate posterior beliefs about the presence of a sensor triggering phenomenon given a path-based sensor observation. We show how the Bayesian update can be leveraged to calculate the expected Shannon information that will be gained along a particular path. We formalize two iterative information-gathering problems that result from this scenario and present path-planning algorithms to solve them. These include: 1) gathering information about the path-based sensor triggering phenomena and 2) assuming the path-based sensor triggering event is “robot destruction,” simultaneously gather information about: 1) hazards using a path-based sensor and 2) information about another environmental phenomenon using a standard sensor, such as the locations of search and rescue targets with a camera. We evaluate our methods using Monte Carlo simulations and observe that they outperform other techniques with respect to the new problems that we consider. Note to Practitioners —This work is motivated by the problem of searching for robot-destroying hazards that are otherwise invisible to the robots. That is, we can observe whether or not a robot survives a path, but, if a robot is destroyed, then we have no idea where, along the path, its destruction has occurred. A mathematically equivalent problem happens in any scenario, in which an agent is equipped with an event sensor that can only be set/triggered once, but that requires postprocessing to figure out if the sensor has been triggered or not. For example, postprocessing is needed if the determination of whether or not a biological specimen was obtained requires a manual laboratory inspection. We also consider an extension of the hazard detection problem, in which we simultaneously collect information about search-and-rescue victims using a “victim sensor,” such as a camera. In this problem, hazards indirectly affect information gathered about victims because new information about victims is lost whenever a robot is destroyed. We provide algorithms to solve these types of problems. The algorithms work even in cases with noise such that false positives and false negatives are possible. This work is useful in any application where observations take the form of a cumulative “yes” or “no” along a path.

A novel optimal route planning algorithm for searching on the sea

ABSTRACTEmergency search and rescue on the sea is an important part of national emergency response for marine perils. Optimal route planning for maritime search and rescue is the key capability to reduce the searching time, improve the rescue efficiency, as well as guarantee the rescue target’s safety of life and property. The main scope of the searching route planning is to optimise the searching time and voyage within the constraints of missing search rate and duplicate search rate. This paper proposes an optimal algorithm for searching routes of large amphibious aircraft corresponding to its flight characteristics and sea rescue capability. This algorithm transforms the search route planning problem into a discrete programming problem and applies the route traceback indexes to satisfy the duplicate search rate and missing search rate.

The Impact of Historical Building Environmental Hazard Rescue Exercises on the Concept of Firemen

In this experiment, the firefighters perform the cultural assets disaster rescue drills before and after the cognitive differences as the core of the experiment. Firefighters develop education in the face of various types of building rescue training, but they lack the type of historical and historical buildings, which are fundamental in nature. The difference between the rescue target and the rescue tactics, and other types of building rescue methods can hardly be applied. The subject of this experiment is to introduce fire rescue resources (public assistance) after the failure of self-disaster prevention (self-help, co-help), and the research object is based on historical building. After many field surveys, knowledge training and actual exercises, the comparison is performed. The fire extinguishing education and training research on historical monuments can improve the effectiveness of historical monument fire extinguishing and preserve the current status of historical monuments, can list the number of cultural relics rescue personnel from 30% to 60%, can increase the number of cultural relics from 20% to 50%, can answer the answer to the room rescue from 20% to 100%, can answer the perimeter protection focus from 40% increase to 100%, can say that the focus of the water is increased from 30% to 100%. From the data, through the drills, the correct rescue concept can be greatly improved, and the secondary damage of historical sites can be avoided, but the focus of the rescue is on the cultural relics. It still needs to be strengthened, so the drills still need to be handled with perseverance in order to be more implemented and popularized.

Up regulation of miR-96-5p is responsible for TiO2 NPs induced invasion dysfunction of human trophoblastic cells via disturbing Ezrin mediated cytoskeletons arrangement

Titanium dioxide nanoparticles (TiO2 NPs) are used extensively in our daily lives, and their toxic effects on the placenta have been reported. Animal studies indicated that placental development is impaired after maternal exposure of TiO2 NPs, but the underlying mechanisms remain largely unknown. In the present study, we used a human trophoblast-derived cell, HTR8-SVneo, to determine how TiO2 NPs affected placental functions, and found out potential reversal targets. TEM was employed for TiO2 NPs morphology observation and uptake assessment. RT-PCR was used to detect the expression of both mRNA and miRNA, and western blotting was used for protein examination. Cell invasion ability was evaluated by Transwell assay, and cytoskeletons were observed by immunofluorescence combined with confocal microscope examination. We found that TiO2 NPs disrupted cytoskeletons and impaired cell invasion ability. Further investigations showed that TiO2 NPs increased the expression of a microRNA (miR-96-5p), which targeted and down-regulated the translation of EZR mRNA, a gene that encodes ezrin protein, and affected the cell cytoskeletons and ultimately cell invasion ability. When the expression of miR-96-5p was down-regulated, the expression level of ezrin protein was also reversed, and cell invasion ability was partially restored. Collectively, we determined how miR-96-5p mediates TiO2 NP-induced placental dysfunction, and provided a potential rescue target for future therapy.

Long-term Outcomes with Rescue Target Deep Brain Stimulation in Patients with Dystonia (P4.059)

Long-term Outcomes with Rescue Target Deep Brain Stimulation in Patients with Dystonia (P4.059)

Improved Detection of Human Respiration Using Data Fusion Basedon a Multistatic UWB Radar

This paper investigated the feasibility for improved detection of human respiration using data fusion based on a multistatic ultra-wideband (UWB) radar. UWB-radar-based respiration detection is an emerging technology that has great promise in practice. It can be applied to remotely sense the presence of a human target for through-wall surveillance, post-earthquake search and rescue, etc. In these applications, a human target’s position and posture are not known a priori. Uncertainty of the two factors results in a body orientation issue of UWB radar, namely the human target’s thorax is not always facing the radar. Thus, the radial component of the thorax motion due to respiration decreases and the respiratory motion response contained in UWB radar echoes is too weak to be detected. To cope with the issue, this paper used multisensory information provided by the multistatic UWB radar, which took the form of impulse radios and comprised one transmitting and four separated receiving antennas. An adaptive Kalman filtering algorithm was then designed to fuse the UWB echo data from all the receiving channels to detect the respiratory-motion response contained in those data. In the experiment, a volunteer’s respiration was correctly detected when he curled upon a camp bed behind a brick wall. Under the same scenario, the volunteer’s respiration was detected based on the radar’s single transmitting-receiving channels without data fusion using conventional algorithm, such as adaptive line enhancer and single-channel Kalman filtering. Moreover, performance of the data fusion algorithm was experimentally investigated with different channel combinations and antenna deployments. The experimental results show that the body orientation issue for human respiration detection via UWB radar can be dealt well with the multistatic UWB radar and the Kalman-filter-based data fusion, which can be applied to improve performance of UWB radar in real applications.

A temporal accessibility model for assessing the ability of search and rescue in Nansha Islands, South China Sea

Time is an indispensable means in accessibility expression and also an important index of the search and rescue (SAR) ability to a country. In this paper, a method for forecasting the location and probability of patrol vessel which is responsible for SAR on the sea is presented; and a temporal accessibility model for assessing the SAR ability of countries around South China Sea in Nansha is structured. In this study, 19 islands and cays in Nansha Islands are selected as rescue targets; eight ports in four countries around South China Sea are selected as SAR bases; patrol vessels belong to coast guard or maritime agency of countries around South China Sea whose displacement is over 1000 tons are selected as principal searchers and rescuers. Based on the density of ships passing the area and the distance from ports of each homeland, the distributions of patrol vessel which belongs to four countries around South China Sea are obtained. The SAR time of China, Vietnam, Malaysia and Philippines in Nansha were calculated based on the proposed method. The results indicated that due to the number of big patrol vessels, China Coast Guard is able to get to Nansha in minimum time, only half an hour for SAR; Vietnam gets second, one and a half hours; Malaysia gets third, 4.6 h; Philippines costs almost 7 h at the bottom of the list, because of the distance and the number of vessels. Taking consideration of the scattered distribution of islands, rocky coral reefs, bad weather and complex marine conditions, it is a very difficult job for SAR vessels. So it is necessary to set up several sub centers and shelters on appropriate islands or atolls in Nansha. And more importantly, how to enhance connection and coordination among countries around South China Sea, build harmonious and smooth SAR mechanisms or channels for cooperation, and improve the ability for solving emergency accidents in Nansha and even the whole South China Sea.

Structure Design and Analysis of Rescue Robot

It becomes the urgent and necessary to the development about wide range of manufacturing a multi-functional and human intelligence rescue robots because of difficulty of rescuing the wounded person in a disaster such as earthquakes and other disasters. A rescue robot prototype has been designed, assembled and commissioned based on the rescue mission and rescue needs of the students in Zhejiang Province mechanical design contest. The rescue robot is able to implement going through the tunnel and the bridge, removing the rescue objectives and other actions tasks. The rescue robot has a structure of four-wheel drive, variable center distance which can improve the ability of walking on the bridge and grabbing the rescue target by suction cups to complete the contest tasks. Experiment verified that the design of actuators and control system is reasonable. It took a total of 1 minute 18 seconds to complete the rescue work in Zhejiang Province mechanical design competition.