TW201118764A - Ship dynamic mode of operation of early warning systems for windows - Google Patents

Abstract

Criteria of ship escape are classified into 4 groups depending on the resulting factors, such as typhoon's scale, path, wind speed, position of typhoon's center and the distance between the typhoon and the interest point. The relationship between the criteria of ship escape and the resulting factors is established by artificial neural network (NN) considering 4 kinds of paths and 50 typhoons chosen to train the model. The accuracy of the proposed NN model is examined using recorded events of ship escape in 4 typhoons. Fair agreements in calibrating and verifying stages show that the proposed model can be applicable for real operation in the future. The proposed ANN model can provide an alert model of ship escape. A basic GUI system is modeled to easily operate the proposed ANN model for the engineers for the future. A risk analysis is introduced to estimate the accuracy of the model result.

Description

201118764 VI. Description of the Invention: [Technical Field] The present invention relates to an early warning mode established by using a neural network, in particular, a predictable ship dynamic and having a graphical user interface (GUI) Early warning mode. [Prior Art] Taiwan is located in the Pacific Ocean, where it is vulnerable to typhoons or unusual waves. The huge waves caused by typhoons are an important factor in destroying the harbor. It also plagues the management of ship operations and dependencies in the port. ...therefore, the stability of the port is related to the management of vessel bowling and cargo handling operations. In view of the importance of material or abnormal wave material bay facilities and vessel bowling, it is necessary to establish a ship dynamic management system that can respond immediately, and provide the basis for vessel parking, loading and unloading operations and departure inspection. [Summary of the Invention]

It is an object of the present invention to provide a visualized operational mode of a ship dynamic early warning system for predicting a ship's changing condition. The invention discloses a windowing operation mode of a ship dynamic early warning system, a material integration module, a group of neural networks (JTHNN) processing module M, a storage module, and a display module. The shell material & module, for a pass to the user input a parameter, and the parameter is performed, the neural network processing module ^ ^ ^ even, and the parameters into the operation, storage mode Group, save (4) neural network processing model - data; display module, display the data stored in the 201118764 storage module. The window operation mode of the ship dynamic early warning system is used to predict an index of Ship Escape (ISE). [Embodiment] In an embodiment of the present invention, the Hualien Port Authority continuously observes the Hualien Port Basin. The extent of the impact of the typhoon, the extent of the impact on the harbor basin depends on the path of the typhoon, the intensity, the extent of the storm circle and its speed of travel. In this example, a total of 82 typhoons from the 85-94 period in the eastern Taiwanese waters to the Hualien Port Basin The impact of the situation is summarized in seven path classifications. The seven typhoon path classifications are shown in Table 1. Table 1 Classification table of typhoon path of Hualien Port Authority

Typhoon path Typhoon name number 1 CAM (Kem), NOGURI (Norocal), NANGKA (South Carolina), CONSON (Conson) 4 2 BOPHA (Baofa), NARI (Nari) 2 3 BETH (Beth ), ERNIE, BABS, FAITH, IMBUDO, KROVANH 6 4 AMBER, YANNI, BILIS Liss), LEKIMA, TORAJI, TRAMI, MORAKOT, MINDULLE, NOCK_TEN, HAITANG, LONGWANG ), TALIM 12 5 HERB, WINNIE, ZEB, KAI_TAK, PRAPIROON, YAGI, RAMMASUN , KUJIRA, MAEMI, MEARI, MEGI, RANIIM, SONGDA, TOKAGE, KHANUN 15 6 GLORIA, SALLY, DAN, MAGGIE, SAM, CHEBI, UTOR, DUJUAN, MELOR (Miller), KOMPASU, OTTO 11 201118764 AERE, DALE, VIOLET, ZANE, ISA, IVAN (Avon), LEVI, OPAL, PETER, ROSIE, TINA, VICK, BART, GLORIA , KATE, OLGA, DAMREY, KIROGI, XANGSANE, CIMARON, HAIYAN, ETAU, LINFA (Lotus), LUPIT (Lu Bi), SOUDELOR (Sudile), CHABA (Jia Ba), MA-ON (Saddle), NIDA (Ni妲), SUDAL (Suda), NABI (Nabi), NESAT ( Nisa), SONCA (Sangka)

From Table 1, it is found that the seventh path has a maximum of '32 in total; the second is that there are 15 in the fifth path, and the first, second, and third paths are the least, and the path classification is as follows: Path 1. South of southwestern Taiwan The typhoon generated by the Chinese Sea is caused by the bus strait going northeastward into the southeastern Taiwan Sea and continuing away from Taiwan, and it will not cause resonance to the Hualian Port. Path 2: The position generated by the offshore of the northeastern Taiwan is approaching Taiwan in the southwest and has not directly invaded Hualien. It does not cause resonance in the Hualien Port. Path The typhoon generated by the eastern waters of Songdo Island is heading westward. The people passing through Luzon Island do not cause resonance in Huahua Liangang Port. Path 4. In the Luzon gas of the Philippines, 6 piles of hardships, the typhoon generated by the eastern waters of the Yima River to the northwest ^ Lian, the most serious impact. Trail 5, in the Philippines, the Luzon bird is closer to Taiwan and then turns north-northwest or the sea area (4) to the northwest is very significant. Holding the North by Hualian, the influence of the sea, the path is also 6: The typhoon generated in the Shufang sea area in Luzon, Philippines, is facing the northwest 201118764. The southern end of the road or the passage of the bus strait, the impact is also known as the Ming • social non-legal Lu Du I I from the North in the distance from Taiwan when the sea: the hurricane generated before the turn to the north or north or north by the Taiwan ^ over = Those who are not close to Taiwan's east and northeast and away from eastern Taiwan / pass '* to turn north and north. ° /, also slightly affected or even shadowless

Among them, the typhoon wave from the path i to the path 3 to Hualien Port is very large to the Hualien Port. The above classification method is the Hong Kong service. The two types of waves: the management of the ship is self-developed. Since this classification is based on the :=:= minute_wind path, it is tested by the Central Meteorological Bureau. a is used as a mode input parameter, so this embodiment uses this knowledgeable mode for mode construction. The classification of typhoon wind speed is shown in Table 2. This embodiment is in accordance with

According to the classification standard of Safflr-Simpson, the typhoon scale is divided into five levels according to the typhoon center air pressure and the maximum wind speed, each of which has about 9 to 18. Table 2 Saffir: Simpson's running wind scale classification standard running wind scale pressure (mb) wind speed (knts) level 1 Zhao Feng > 980 64 ~ 82 level 2 typhoon 965~980 83 ~ 95 level 3 typhoon 945~965 96~112 Level 4 Hurricane 920~945 113~134 Category 5 Hurricane <920 > 134 According to Table 3 Beaufort Scale, the higher the wind speed, the higher the wave height, and the wind speed has a significant relationship with the wave height. In this implementation, 201118764, the wind speed is also included in the impact factor.

Use > test the first picture, the first! The figure shows the distance from the observation center of the typhoon center to the observation station. (Significant Wave Height, Hs) = relationship diagram. From the i-th diagram, it can be found that the wind distance is not significantly related to the typhoon when it is on the i-meter. If there is a clear relationship within 1500 km. The dynamics of the ship in Hualian Port in this embodiment are as follows: During the operation period, the ship is divided into four types of vessels. (1) The boat of Hualien material and its loading and unloading work are unaffected. 201118764 (2) There are swells in the port of Hualien, but the swells are not so large that they affect loading and unloading operations, and vessels do not need to leave the port to avoid the wind. (3) The swell generated is so large that the ship only needs to leave the port to avoid the wind, so as to avoid the possibility of cable breakage. (4) The vessel has broken the cable. A cable breakage in a vessel may cause the vessel to drift freely in the port and cause a more serious vessel change, such as a possible collision with other vessels in the port. Therefore, we attribute the cable to the most severe vessel transaction index 4, and based on the severity of the vessel movement, it will be defined as four levels of Index For Ship Escape (ISE) ° based on the four vessel transaction index 82 typhoons are classified as shown in Table 4. It can be seen from Table 4 that 27 of the 82 typhoons caused the ship to be broken, 25 typhoons did not affect the ship, and 12 typhoons caused swells. The 18 typhoon boats only need to be out of the harbor to avoid the wind. Table 4 typhoon vessel transaction index classification table vessel transaction index ISE typhoon name number 1 BART (Bart), BETH (Beth), BOPHA (Baofa), CAM (Kem), CIMARON (Ximalong), CONSON (Conson), DAMREY, ERNIE, FAITH, GLORIA, HAITANG, HAIYAN, KATE (|iL 4 Temple), KOMPASU (Compaq) Λί ), KROVANH, LEVI, MEGI, MELOR, NANGKA, NAR1, NOGURI, PRAPIROON Billenn, RANIIM, TRAMI, VICKI 25 2 LEKIMA, L1NFA, MA_ON, MAEMI, MEARI ), MORAKOT, 12 201118764 NESAT, OLGA, PETER, SONCA, TINA YANNI 3 CHABA , CHEBI, DALE, DAN, DUJUAN, KAI_TAK, KHANUN, KIr5gi (4 Rocky), KUJIRA, KUJIRAMAGGIE, NIDA, ^0(: 反_丁£1^(纳坦), 110 51£(罗西), SAM(萨姆), SUDAL(舒达), UTOR(尤尤Special), VIOLET, YAGI 18 4 AERE, AMBER, BABS, BILIS, ETAU, GLORIA ), HERB (He Bo), IMBUDO (Yim Budu), ISA (Lisa), IVAN (Ivan), 1^€^0 from eight \0 (Dragon King), 1^卩1 Ding (Lu Bi), MINDULLE, NABI, OPAL, OTTO, RAMMASUN, SALLY, 50, 00 (Sanda), 50110 £ [ 011 (Sudile), TALIM, TOKAGE, TORAJI, WINNIE, XANGSANE, ZANE, ZEB 27

Therefore, in the present embodiment, the vessel transaction index 1 to 4 is defined as follows: Vessel transaction index 1: The vessel in Hualien Port and its loading and unloading operations are unaffected. Vessel Change Index 2: There is a swell in the Hualien Port, but the swell is not large enough to affect the loading and unloading operations, and the vessel does not need to leave the port to avoid the wind. Vessel Change Index 3: The swell generated is so large that the ship only needs to leave the port to avoid the wind, so as to avoid the possibility of cable breakage. Vessel Change Index 4: The vessel has broken the cable. Next, for the typhoon wind speed and the azimuth angle of the typhoon center to Hualien Port, analyze the vessel movement index. Please refer to Figures 2-1 and 2-2. Figure 2-1 shows the relationship between the typhoon wind speed and the ship's transaction index, and Figure 2-2 shows the relationship between the typhoon center-to-station azimuth and the ship's transaction index. From the 201118764 2-1 chart, the ship's transaction index 1 distribution can be seen. In the range of 75~175knots, the index 2~4 has a wind speed distribution in the range of 55~295 knots, and the wind speed of the typhoon wind speed index 1 of index 2~4 is less discriminating, indicating that the typhoon wind speed must not directly affect the vessel movement. In addition, it can be seen from Figure 2-2 that the dynamics of Hualien Port are affected by the latitude of Hualien Port, which is ~150 degrees and -60~0 degrees, and the distribution range of each index is similar. From the relationship between wind speed and azimuth to the transaction index, it can be seen that the distribution of the two is relatively scattered, although there are no rules and it is difficult to identify 'but the two parameters are still qualitatively describe the relationship affecting the transaction index.

In addition, the distance between Typhoon and Hualien Port (£), the maximum wind speed of typhoon (匕-X), the angle from Typhoon Center to Hualien Port (0 〇, typhoon azimuth 2), wind field energy n n (Fma , /Z sigh (D)) and the typhoon factor affecting vessel dynamics, such as the sea and land warning issued by the Central Meteorological Administration (F/, Waring Index), establishes the relationship between typhoon and ISE, and the related factors are shown in Figure 2d. Figure 2-3 shows a schematic diagram of the typhoon factor affecting ship dynamics. Among them, the typhoon warning at sea is defined as: Typhoon 7-level storm circle 24 hours 2 When entering Taiwan or the Golden Gate 'Malay coastline within 100 kilometers of the sea area, △, then every 3 hours to report, if necessary, add it. The sea and land hurricane alarm is defined as: off (4) when the storm circle enters Taiwan, Jinmen, and Mazu land after ten hours, and then reports every hour. When necessary, the same as the release of the land hurricane (4), not subject to the above conditions, the regulation of the Bay & ^ wind alarm is defined as: when Zhao Feng's seven-level storm winds leave: Kinmen, Maji land 'but still not When it is a hundred kilometers away from the sea, the change is lifted:: Police: When the hurricane is determined to leave - 100 kilometers offshore, when the release of the turn or dissipate, you must directly solve the _ wind alarm. 201118764 In this embodiment, the applied neural network has the function of learning optimization' and establishes its correlation through the connection between its mode output value and the learning target value, using network weights and thresholds. The value of the bias is the strength of the relationship. In the present invention, the selected network algorithm is a back-propagation method. Since the reverse transmission network has a monitoring function, the interaction between the neurons obtains the mode output value through the learning method. The minimum root mean square error from the actual value. In this paper, the neural network uses ♦ MaUab software to build a reverse-transfer-like neural network. As shown in Figure 3, Figure 3 shows a schematic diagram of a neural network architecture. The inverse transfer learning algorithm is a widely used learning algorithm. Because it has the function of learning and recalling, it can be predicted in the same way as the learning process in the process of recalling. The general inverted network can be divided into three parts. The input layer is used to accept the information of the external environment. The hidden layer (5) is called to represent the mutual input and output layers. Relationships, and the correlations are described by weights and thresholds: the output layer is used to output messages to the external environment. The inverse transfer network learning method is to input a considerable number of learning samples, apply forward feed and error backward correction two steps, deduct the internal mapping rules of input variables and output variables, and then apply the recall function to output the new case. Variable numerical estimation. The network of the architecture of Figure 3 is a layer of hidden layer and an output layer. It is a conversion function, sh is the number of neurons in the hidden layer and the output layer, and plays the role of inference. The process of mapping through the conversion function is the input variable matrix. W is the weight and 阙 matrix between the input layer and the hidden layer, ^ 12 201118764 and Bu is the weight and threshold matrix between the output layer and the hidden layer. Through the process of network learning error optimization, the neural network records the relationship between the input value and the learning target value on the inter-value and weight, thereby indicating the relationship between the input value and the learning target value r::. Among them, the neural network uses a conversion function to make the hidden layer mimic the function of the biological neuron to deal with nonlinearity, and the weighted product of the input values is the output value of the processing single 7". The relationship between the hidden layer and the input parameters and the output layer of the entire inverted network mode can be expressed by the equation as Λ Λ,, χΐ - Λχ, {lWtSXn Pnxl + bUsx]) (1)

A2,r,l ~/rxl(^,rXiei ixI :in...is the output matrix of the hidden layer' illusion as the inverse output network output value matrix, / is the conversion function. It is obtained by the operation of the child The network output value matrix, which is defined by the learning target matrix ^ edge defined by the two matrices (N_) is the error function £, which represents J~-y«2 N (3)

Therefore, 'the whole network learning is overturned A ^ ^ . (3) The over-privacy in order to optimize the weights and thresholds between the neurons to make the error function reach the minimum value of the political value of the mountain The process brings the network output value closer to the learning target value. The network ^ 1 'to establish the most appropriate weights and thresholds for the learning process (optimization process) is the process of recalling the network. ^Introduction to the heart. The following describes the calculation process, the optimization process can be divided into two types - "for heuristic optimization" such as variable learning rate ("annng rate" and (four) (Resilient) The calculation *; another - to use the "quasi-value optimization, such as conjugate ladder .u, pick gradient method, quasi-Newton method and - Μ, method. In general, the Levenberg-Marquardt algorithm will have the fastest network convergence (Neural Network Toolbox User’s Guide) on the 13 201118764 problem of function approximation. Therefore, this paper chooses to use LM algorithm as the optimization process to minimize the network error function. The algorithm uses Hessian matrix approximation to achieve the best purpose, and the following formula represents ^k+\ =^k ~\ JTJ + ^]~lJTe (4)

Wherein, the edge, the illusion·/ respectively represent the optimization value of each iteration weight {heart, ^^ and the threshold value {h,~}, and y is the Jac〇bian matrix, which contains the network error and weight. The first-order differential of the value and the threshold, e is the network error vector, and / is the unit vector. When 〆=〇 of equation (4), it approximates the Hessian matrix of Newton's method. When the value of 〆 is large, equation (4) has a gradient of smaller steps. The purpose is to move as fast as possible. Newton's method is used to optimize the network error, and finally the optimal weights and thresholds for the network can be obtained. Therefore, the inverse transfer-like neural network calculates the mode output value through the conversion function mapping mode via the formula (丨) and the formula (?), and uses equation (4) as the optimization of the weight and the threshold value. The standard for completing the network learning is judged by the formula (3). ^ According to the analysis of the dynamic data of the vessel, the tropical depression has not affected the Hualien port vessel, so the typhoon described affects the dynamic parameters of the vessel, the distance between the wind and Hualien port (9), and the maximum wind speed of the hurricane (U, wind center The angle of Yanghua Port (61丨), the azimuth of typhoon (Θ 2), the energy of the wind field and the sea issued by the Central Meteorological Administration, the running wind factor affecting the dynamics of the ship and the Central Meteorological Bureau (4) report = the next 48 hours The forecast data is used as the round-in value of the neural network. 201118764 This model will be published by the Meteorological Bureau or JTWC as the relevant input value of the neurological model, and the second parameter will be the fixed area _ wind and ship movements The wind data Z), r, e can be expressed as a time series combination (3-5) of the 'θΐ, θ2ϋ function as a function of the system s / 丨. · For time.

The learning example selected in this embodiment is the cover type 4 Wei Feng type, 4 kinds:: and the change index 'to select the wind to be a total of 5 (sturdy, as a nerve-like learning model: the present embodiment uses two hidden layers to The effect of non-linearity is as follows: the network architecture is 6 input neurons, and the output layer is !, the value is: and the test results of the test are 'the hidden layer of the second layer of this embodiment, the neuron=80 and 40. The hidden layer transfer function uses Log sigmoid transfer functi〇n, and the final iteration number is set to 1500 generations, any condition where the error square is approximated to 〇 or the number of iterations is Η(9) generation. 'The network stops learning, but the invention should not be limited to this. The graphical user interface (GUI) environment of this embodiment is GUIDE in Matlab (Graphicai User Interfaces)

Devel〇pment Environment). The operating environment is Mathw〇rks's technology computing application software Matlab, and the version supports Matlab 5 〇 or higher. The development goal of the graphical interface is to achieve the most processes and the most information with the fewest operating procedures, which can reduce the switching of windows and improve the smoothness of the operation process. 201118764 In 1 case, the entire graphical interface consists of five windows, divided into sections, and the interface window 2' data input window 3. Data archive window 4 Free wind path selection window 4 · Figure, eight E, person · instant Measured "Figure 6. Instant satellite imagery, respectively, as follows: ^ Like 1. The main interface window is in view (see the window title column: the title column clearly lists the current interface on the island and § the main interface version and other information. Scared (7) Interface main window toolbar: The toolbar contains four parts: data input and output, Γ furnace and auxiliary. The layout of the toolbar is mainly in line with the Windows application and the structure of the tree structure. Store the deductions to be selected, such as the first and main windows. If the picture is not shown in Figure 4-1, the display interface is shown in Figure 4-2, and the 4_2 figure shows the structure of the tree structure. And the column 'the sub-tools are the input and output parts including the input of the typhoon data, and the output of the transaction index and leaving the interface. The second sub-tool simulation part, this part is the data to the '枓 input is correct Choose the appropriate class god The structure is simulated. After the simulation, the vessel's transaction index report is collected. The third sub-tool is listed as the Hurricane Information section to obtain instant typhoon information through the Internet. For explanation and copyright announcement, including the complete user manual of the call and the version information window about the interface, the opening of the user manual will be called the external program to open the HTML data browser. (1) Change the hurricane distance of the input data : This area can instantly calculate the distance between the wind and the port calculated by the entered wind position data and 16 201118764. The material and the closest point of the hurricane help the user to know the time of the entered typhoon. (4) The wind speed change map of the deaf person data: This area can be used to display the wind and wind speed data in real time. It helps the user to instantaneously and does not know the time when the maximum wind speed occurs. 0 (5) Typhoon path Figure: The typhoon path window shows the hurricane travel path of the typhoon at 15 30 degrees north latitude and 4 to 138 degrees east longitude. Dynamic & number. This is the model for the model to read the hurricane information and then simulate the ship dissimilar material n in the mode estimation result reaches the level 3 刖A hour release alarm sound effect 'the red vertical line in the figure shows the alarm release time, The green vertical line indicates the current time. In this embodiment, the current data is used to calculate the ship's transaction index, and the current time is still compared with the current time of 15#, the second time, and the alarm is issued when the green vertical line coincides with the red vertical line. (7) Warning light: With the different transaction index of the vessel, the flashing tri-color lamp is used to release the current situation. When there is no signal, the vessel's transaction index is i, that is, the vessel in Hualien Port and its loading and unloading operations are unaffected. The green light indicates that there is a vessel transaction index of 2, and there is a swell in the Hualien port, but the swell is not so large that it affects the loading and unloading operations, and the vessel does not need to leave the port to avoid the wind; the yellow light represents the vessel's transaction index 3, and the swell generated in the port is large enough to the vessel. It is necessary to avoid the wind in order to avoid the possibility of cable breakage; the red light indicates the ship's transaction index 4, and the historical data used in the training according to the mode indicates that the ship has broken the cable. At this time, it is quite dangerous to represent the mooring vessels in Hualien Port. In addition, on the right side of the warning light and provide the volume of the user's adjustment of the 2011 18764 full alarm sound. (8) Warning sign: Use the method of flying in to remind the user of the current level of the ship's transaction index and the recommendations of the ship's dynamics. There are four warning signs, as shown in Figure 4-3. Figure 4_3 shows the ship's transaction index. Corresponding warning signs. In one embodiment, the right side of each warning sign indicates that the vessel transaction index is represented by Level 1 to 4. The icon on the left side roughly indicates the current state of the vessel in the port, and LeVel is the static state of the water in the harbor; Level 2 is a surge. Waves still do not affect the loading and unloading operations of the vessel; Level 3 means that the thirsty waves generated in the port are so large that the ship can only avoid the wind and the wind can be removed, and the Levei 4 indicates that the vessel may have broken the cable, which is very dangerous. 2. Data input window As shown in Figure 5.1, the data input window is similar to the general Wind〇ws file open window. The operation details are detailed below: (1) Current folder location: display current folder, user In this area, you can switch between the storage devices or network storage devices located in the computer to change the current folder location. (2) Back to previous page button: This button function is to return to the previous directory 0 (3) Back to the previous layer button: This button function is to return to the upper layer of the current folder - if it is in the top folder It will go back to the root directory of the disk, and the upper layer of the Windows operating system root directory is my computer. (4) Add folder button: This button can add a new sub-folder in the folder where it is located. (5) View mode change button: This can be used to change the current data in 201118764, and the performance mode is appropriately changed, including large map #, small map, list, detailed information and thumbnail. (6) 35 Name field: The user can input the name of the input data file to be opened in this area. —(7)it file type: This area can set the type of the slot in the file list area. The interface is defined here to display all files with the file name *over, and the user can change it to display all files as needed. (8) 档案 档案 File button: This button can perform the action of opening after selecting the file ’ and return to the main window for the next step operation and check. (9) Take the 4 button and leave the window without making any changes. _Shaw case list area: List the (4) case in this folder and sub-1) Description button 'When this button is pressed, the mouse cursor will be the main question mark and then the various components in this window will appear. Brief description (12) Close the window button: Same as the cancel button, open the window without making any changes. 3. Data archiving window: No. 5 2 is not shown. Figure 5_2 shows the simulation results data archived by the lesser ', 歹^ input and output', in the "archive storage," can be carried out on the vessel, Aberdeen®, window The components in the description are the same as: 'Material input window. The #宏扪棕 case format restrictions and changes in the slot are: In the figure (7) slot type area, Λ ^ 埤 two different windows will be archived t format is restricted, the user's helmet must be...Bei Du ~ will store ascii's data file; Miscellaneous format. 19 201118764 4. Typhoon path selection window ^ When the data input is completed, you can select the upper tool column, The pattern calculation in the mode calculation is used to perform the mode estimation. This real mode automatically lists the 7 paths to provide the user with a choice. Please refer to Figure 5·3, Figure 5_3, '', typhoon road selection window'. The seven classifications are determined by the Hualien Port Authority in Table 2. The following is an example of the complete operation of the model: Available tools in the Matlab environment The slot open function of the column or the direct input of C_and WindGW is the name of the interface. The name of the block is currently tentatively set to mship.p (as shown in Figure 6). After entering the (4) dynamic warning mode GUI, I saw that there are four tiles in the main work area, as well as the warning light, but the simulation calculation cannot be displayed by the input data of ...s and Wo. In the upper toolbar, this sister, & Select, read the capital for the input of the bait, or select the "instant wave height and instant satellite imagery in the external Weidan m 来 to query the data" and the palace soil, and the Gan and the "he" The user manual,, and, copyright H has its options such as "data storage, and,, mode operation". This is to avoid the user's wrong fall = anti-white state can not be selected, report. In this step we can choose "Input and: fruit Read data. And according to Zhao Di, w into the shell material ', enter the window to select the input data. In addition, the default switchable name in the hard disk of the host computer or the network hard disk in the figure 6.3 is (*.dat), and the user can adjust the file name of the file. Type (") to find the name of any ascii file that you want to enter. 2011. The current typhoon data input format uses the simplest plain text file. The file contains the following typhoon data: I Temple Gate 歹丨 2. Year 3. Month 4.曰5.6.Longitude 7.Latitude 8·Wind speed 9·Zhao Feng alarm status, the format is not strictly limited, users can use EXCEL or document editing software to make this input file, in each column of data Tab, Space, comma, etc. can be used to indicate the interval. If there is any omission in the data, the operator needs to do the interpolation or addendum processing first. After the processing, adjust the time series (from the beginning to the last data) to start the input. • If the data length and format are not problematic after the typhoon data input is completed, the surface of the wind center, the maximum wind speed, the distance between the hurricane center and the Hualien port, and the typhoon path map will be automatically displayed in the window block of the interface. Figure 6_4 while viewing Photo after the issue can not be ,, click op mode in the toolbar projections after the "path selection to start the simulation projections. As shown in the figure, the situation after the completion of the calculation is shown. The lower right of the interface is the change of the ship's transaction index with time, and the interface is not marked by the warning light. After the calculation is completed, you can use the upper toolbar, and input and export the data in the "out" to save. In summary, the windowed operation mode of the ship dynamic early warning system not only provides the user with the convenience of operation, but also integrates the operation of the whole mode. The mode after the sigma can be used to improve the cooperation degree of the future automation environment. The future can be applied to provide online real-time ship dynamic mode estimation, which can directly extract typhoon observation data in an unmanned environment to calculate the ship's transaction index and simultaneously publish it with the server.

[S 21 201118764 [Simple description of the diagram] Figure 1 shows the relationship between the indicative wave heights of the typhoon center. The distance between the station and the observatory station shows the relationship between the wind speed and the ship's transaction index. I do not have a typhoon center to the station orientation index diagram. also

The figure shows a schematic diagram of the typhoon factor that affects ship dynamics. The diagram shows a schematic diagram of a neural network architecture. Figure 4-1 shows the main interface of the interface. Figure 4-2 shows the toolbar of the tree structure. Figure 4-3 shows the warning signs corresponding to the ship's transaction index. Figure 5-1 shows the typhoon data input window. Figure 5-2 shows the simulation results data archive window. Figure 5-3 shows the typhoon path selection window. Figure 6-1 shows the operational flow of the present invention - an embodiment. Fig. 6-2 shows the operational flow of an embodiment of the present invention. Fig. 6-3 shows the operational flow of an embodiment of the present invention. Fig. 6-4 shows the operational flow of an embodiment of the present invention. Figures 6-5 show the operational flow of the present invention - an embodiment. [Main component symbol description] (No component symbol description) 22

Claims (1)

201118764 VII. Patent application scope: ι_ A window operation mode of the ship dynamic early warning system, including: - the bedding integrated module for the user to input the parameters and integrate the parameters; a type of neural network (Artificial Neural The network (ANN) processing module performs the operation of the parameter; the storage module stores one of the neural network processing modules; and the display module displays the data stored by the storage module. " The windowed operational mode of the ship's dynamic early warning system is used to predict a Ship For Ship Escape (ISE). 2. The window operation mode of the ship dynamic early warning system as described in the third paragraph of the patent scope of Zhongqing, wherein the neural network processing module has the function of learning optimization, and through the 输出 output value and the learning target value The link establishes its relevance, using the weights of the network and a bias to indicate the strength of its relationship. For example, in the window operation operation type of the ship dynamic early warning system described in item 2 of the patent scope of the application, the neural network processing module uses a reverse transfer (Back_pr〇pagati〇n) algorithm to perform the calculation, and the inverse transfer algorithm is supervised. Learning, by the interaction of a plurality of gods and 4 elements, is obtained through learning methods - the minimum root mean square error. For example, the window dynamic operation mode of the ship dynamic early warning system described in claim 3 of the patent scope, wherein the minimum root mean square error is the minimum root mean square error of a mode output value and an actual value. For example, the window operation mode of the ship dynamic early warning system described in item 4 of the patent scope of the application, wherein the parameter includes: a plurality of typhoon maximum wind speeds, a plurality of pen table winds, and a plurality of typhoon wind latitudes. 23 201118764 6. As in the windowed operation mode of the ship dynamic early warning system described in item 5 of the patent application scope, the reverse transfer algorithm has a recollection process and a learning process, in the same manner as the learning process in the process of recalling Make predictions. 7. The windowing operation mode of the ship dynamic early warning system as recited in claim 6 of the scope of the patent application, the reverse transfer algorithm comprising: an input layer for accepting information of an external environment; a hidden layer; and an output layer; For outputting a message to an external environment, the hidden layer is used to represent the relationship between the input layer and the processing units of the output layer, and the correlation is illustrated by the weight and the threshold; The transfer algorithm is based on the input-learning sample, applying the forward-feeding and correcting two-step 'pushing an input variable and an internal mapping rule of the output variable' and then applying the recall function to perform a new case round-out variable value. Estimated. 8. If you apply for the patented operation mode of the ship dynamic material system as described in item 7 of the patent, the study sample includes: hurricane and port distance (9), maximum wind speed ([_), angle from the wind center to the port (10) , hurricane azimuth ((6), wind field energy, sea issued by the Central Meteorological Administration, land warning (fFZ), and forecast data for the next 48 hours of running wind. 9. Ship dynamic warning as described in item 8 of the patent application scope The system is in a windowed operation mode, and the display module is a graphic user interface (GraphieaiUsef Interface, GUI). Η). The application for the windowing operation of the ship dynamic early warning system described in item 9 of the patent application scope, the vessel transaction index is Typhoon and port distance (7)), typhoon maximum wind speed (D, hurricane center to port angle (θ0, hurricane azimuth ((5), 24 201118764 wind field energy &(La; t/Iog (D)), central Time-series combination of functions of sea and land warnings (orders) issued by the Bureau of Meteorology. 25