VEHICLES 翻译

时间: 2023-11-21 11:06:25 浏览: 129
"VEHICLES"可以翻译为“车辆”。车辆是用于运输人员或货物的机动车辆,包括各种类型的轿车、卡车、客车、摩托车等等。在日常生活和工业生产中,车辆是不可或缺的交通工具和设备,它们极大地方便了人们的出行和物流运输,也促进了经济和社会的发展。同时,车辆的制造、维修和管理也涉及到各种工程、技术和管理领域,是一个综合性很强的领域。因此,车辆是一个非常重要的词汇,它代表着现代交通运输和机械工程的技术和进步。
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翻译XEP_VEHICLES

XEP_VEHICLES 可以翻译为“车辆”。在汽车制造和维修过程中,车辆是指用于运输人员或货物的机动车辆,通常包括各种类型的轿车、卡车、客车、摩托车等等。车辆是由各种系统、部件和模块组成的复杂机械系统,包括发动机、传动系统、制动系统、转向系统、悬挂系统等等。车辆的制造、测试、维修和故障排除需要使用各种工具、设备和技术,以确保车辆的性能、安全和可靠性。因此,车辆是汽车制造和维修中非常重要的核心对象之一,它们代表着交通运输和机械工程领域的技术和进步。

计算机复试英文文献翻译

Computer Science Literature Review With the rapid development of computer technology, computer science has become an important and essential discipline in modern society. The literature review is a critical component of research in computer science, as it provides a comprehensive understanding of the current state of knowledge in a specific field. This paper presents a literature review of computer science, focusing on the fields of artificial intelligence, computer networks, and software engineering. Artificial Intelligence Artificial intelligence (AI) is a branch of computer science that focuses on creating intelligent machines that can perform tasks that typically require human intelligence. AI has a broad range of applications, from natural language processing and image recognition to robotics and autonomous vehicles. One of the most significant recent developments in AI is deep learning, which is a subfield of machine learning that uses neural networks to analyze and learn from large data sets. Deep learning has been used to improve speech recognition, image classification, and language translation. Another area of AI research is reinforcement learning, which involves training machines to make decisions based on rewards and punishments. Reinforcement learning has been used in game playing, robotics, and even finance. Computer Networks Computer networks are critical components of modern society, as they enable communication and data exchange between devices and systems. The internet is the most prominent example of a computer network, but there are many other types of networks, including local area networks (LANs) and wide area networks (WANs). One of the most significant recent developments in computer networks is the emergence of 5G technology, which promises to provide faster and more reliable wireless communication. 5G networks will enable new applications, such as autonomous vehicles and smart cities, that require high-speed and low-latency communication. Another area of network research is software-defined networking (SDN), which allows network administrators to programmatically control network behavior. SDN has been used to improve network efficiency, security, and scalability. Software Engineering Software engineering is the process of designing, developing, and maintaining software systems. The field has evolved significantly since the early days of programming, with the emergence of new development methodologies, such as agile and DevOps. One of the most significant recent developments in software engineering is the rise of cloud computing, which allows software to be deployed and accessed over the internet. Cloud computing has transformed the way software is developed and deployed, enabling new models of software delivery, such as software as a service (SaaS). Another area of software engineering research is the development of automated testing and deployment tools. These tools help developers identify and fix bugs more quickly and deploy software more easily and reliably. Conclusion Computer science is a rapidly evolving field, with new developments and innovations emerging on a regular basis. This literature review has highlighted some of the recent developments in artificial intelligence, computer networks, and software engineering. As the field continues to evolve, it will be essential to keep up with the latest research and trends to remain competitive and relevant.

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feScore - EPA Fuel Economy Score (负1 = Not available) fuelCost08 - annual fuel cost for fuelType1 ($) (7) fuelCostA08 - annual fuel cost for fuelType2 ($) (7) fuelType - fuel type with fuelType1 and fuelType2 (if applicable) fuelType1 - fuel type 1. For single fuel vehicles, this will be the only fuel. For dual fuel vehicles, this will be the conventional fuel. fuelType2 - fuel type 2. For dual fuel vehicles, this will be the alternative fuel (e.g. E85, Electricity, CNG, LPG). For single fuel vehicles, this field is not used ghgScore - EPA GHG score (负1 = Not available) ghgScoreA - EPA GHG score for dual fuel vehicle running on the alternative fuel (负1 = Not available) guzzler- if G or T, this vehicle is subject to the gas guzzler tax highway08 - highway MPG for fuelType1 (2) highway08U - unrounded highway MPG for fuelType1 (2), (3) highwayA08 - highway MPG for fuelType2 (2) highwayA08U - unrounded highway MPG for fuelType2 (2),(3) highwayCD - highway gasoline consumption (gallons/100miles) in charge depleting mode (4) highwayE - highway electricity consumption in kwhrs/100 miles highwayUF - EPA highway utility factor (share of electricity) for PHEV hlv - hatchback luggage volume (cubic feet) (8) hpv - hatchback passenger volume (cubic feet) (8) id - vehicle record id lv2 - 2 door luggage volume (cubic feet) (8) lv4 - 4 door luggage volume (cubic feet) (8) make - manufacturer (division) mfrCode - 3character manufacturer code model - model name (carline) mpgData - has Your MPG data; see yourMpgVehicle and yourMpgDriverVehicle phevBlended - if true, this vehicle operates on a blend of gasoline and electricity in charge depleting mode pv2 - 2door passenger volume (cubic feet) (8) pv4 - 4door passenger volume (cubic feet) (8) rangeA - EPA range for fuelType2 rangeCityA - EPA city range for fuelType2 rangeHwyA - EPA highway range for fuelType2 trans_dscr - transmission descriptor; see http://www.fueleconomy.gov/feg/findacarhelp.shtml#trany trany - transmission UCity - unadjusted city MPG for fuelType1; see the description of the EPA test procedures UCityA - unadjusted city MPG for fuelType2; see the description of the EPA test procedures UHighway - unadjusted highway MPG for fuelType1; see the description of the EPA test procedures UHighwayA - unadjusted highway MPG for fuelType2; see the description of the EPA test procedures VClass - EPA vehicle size class year - model year youSaveSpend - you save/spend over 5 years compared to an average car ($). Savings are positive; a greater amount spent yields a negative number. For dual fuel vehicles, this is the cost savings for gasoline. sCharger - if S, this vehicle is supercharged tCharger - if T, this vehicle is turbocharged翻译

帮我翻译代码:def splitRoutes(node_id_list,model):V={i:[] for i in model.demand_id_list} V[-1]=[[0]*(len(model.vehicle_type_list)+4)] V[-1][0][0]=1 V[-1][0][1]=1 number_of_lables=1 for i in range(model.number_of_demands): n_1=node_id_list[i] j=i load=0 distance={v_type:0 for v_type in model.vehicle_type_list} while True: n_2=node_id_list[j] load=load+model.demand_dict[n_2].demand stop = False for k,v_type in enumerate(model.vehicle_type_list): vehicle=model.vehicle_dict[v_type] if i == j: distance[v_type]=model.distance_matrix[v_type,n_1]+model.distance_matrix[n_1,v_type] else: n_3=node_id_list[j-1] distance[v_type]=distance[v_type]-model.distance_matrix[n_3,v_type]+model.distance_matrix[n_3,n_2]\ +model.distance_matrix[n_2,v_type] route=node_id_list[i:j+1] route.insert(0,v_type) route.append(v_type) "检查时间窗口。只有在满足时间窗口时才能生成新标签。否则,跳过“" if not checkTimeWindow(route,model,vehicle): continue for id,label in enumerate(V[i-1]): if load<=vehicle.capacity and label[k+4]<vehicle.numbers: stop=True if model.opt_type==0: cost=vehicle.fixed_cost+distance[v_type]vehicle.variable_cost else: cost=vehicle.fixed_cost+distance[v_type]/vehicle.free_speedvehicle.variable_cost W=copy.deepcopy(label) "set the previous label id " W[1]=V[i-1][id][0] "set the vehicle type" W[2]=v_type "update travel cost" W[3]=W[3]+cost "update the number of vehicles used" W[k+4]=W[k+4]+1 if checkResidualCapacity(node_id_list[j+1:],W,model): label_list,number_of_lables=updateNodeLabels(V[j],W,number_of_lables) V[j]=label_list j+=1 if j>=len(node_id_list) or stop==False: break if len(V[model.number_of_demands-1])>0: route_list=extractRoutes(V, node_id_list, model) return route_list else: print("由于容量不足,无法拆分节点id列表") return None

With the rapid development of China's economy, the per capita share of cars has rapidly increased, bringing great convenience to people's lives. However, with it came a huge number of traffic accidents. A statistical data from Europe shows that if a warning can be issued to drivers 0.5 seconds before an accident occurs, 70% of traffic accidents can be avoided. Therefore, it is particularly important to promptly remind drivers of potential dangers to prevent traffic accidents from occurring. The purpose of this question is to construct a machine vision based driving assistance system based on machine vision, providing driving assistance for drivers during daytime driving. The main function of the system is to achieve visual recognition of pedestrians and traffic signs, estimate the distance from the vehicle in front, and issue a warning to the driver when needed. This driving assistance system can effectively reduce the probability of traffic accidents and ensure the safety of drivers' lives and property. The main research content of this article includes the following aspects: 1. Implement object detection based on the YOLOv5 model. Conduct research on convolutional neural networks and YOLOv5 algorithm, and develop an object detection algorithm based on YOLO5. Detect the algorithm through road images, and analyze the target detection algorithm based on the data returned after training. 2. Estimate the distance from the front vehicle based on a monocular camera. Study the principle of estimating distance with a monocular camera, combined with parameters fed back by object detection algorithms, to achieve distance estimation for vehicles ahead. Finally, the distance estimation function was tested and the error in the system's distance estimation was analyzed. 3. Design and implementation of a driving assistance system. Based on the results of two parts: target detection and distance estimation, an intelligent driving assistance system is constructed. The system is tested through actual road images, and the operational effectiveness of the intelligent driving assistance system is analyzed. Finally, the driving assistance system is analyzed and summarized.

帮我翻译代码:def splitRoutes(node_id_list,model): V={i:[] for i in model.demand_id_list}#代码首先使用字典推导式创建了一个空的字典,并将其赋值给 "V"。字典中的键为需求点的 ID,值为一个空列表。 V[-1]=[[0]*(len(model.vehicle_type_list)+4)] V[-1][0][0]=1 V[-1][0][1]=1 number_of_lables=1 for i in range(model.number_of_demands): n_1=node_id_list[i] j=i load=0 distance={v_type:0 for v_type in model.vehicle_type_list} while True: n_2=node_id_list[j] load=load+model.demand_dict[n_2].demand stop = False for k,v_type in enumerate(model.vehicle_type_list): vehicle=model.vehicle_dict[v_type] if i == j: distance[v_type]=model.distance_matrix[v_type,n_1]+model.distance_matrix[n_1,v_type] else: n_3=node_id_list[j-1] distance[v_type]=distance[v_type]-model.distance_matrix[n_3,v_type]+model.distance_matrix[n_3,n_2]\ +model.distance_matrix[n_2,v_type] route=node_id_list[i:j+1] route.insert(0,v_type) route.append(v_type) "检查时间窗口。只有在满足时间窗口时才能生成新标签。否则,跳过“" if not checkTimeWindow(route,model,vehicle): continue for id,label in enumerate(V[i-1]): if load<=vehicle.capacity and label[k+4]<vehicle.numbers: stop=True if model.opt_type==0: cost=vehicle.fixed_cost+distance[v_type]vehicle.variable_cost else: cost=vehicle.fixed_cost+distance[v_type]/vehicle.free_speedvehicle.variable_cost W=copy.deepcopy(label) "set the previous label id " W[1]=V[i-1][id][0] "set the vehicle type" W[2]=v_type "update travel cost" W[3]=W[3]+cost "update the number of vehicles used" W[k+4]=W[k+4]+1 if checkResidualCapacity(node_id_list[j+1:],W,model): label_list,number_of_lables=updateNodeLabels(V[j],W,number_of_lables) V[j]=label_list j+=1 if j>=len(node_id_list) or stop==False: break if len(V[model.number_of_demands-1])>0: route_list=extractRoutes(V, node_id_list, model) return route_list else: print("由于容量不足,无法拆分节点id列表") return None

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