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[AI-NN-PR] DavidPeterman_C DL : 0: 问题描述：虽然离开浦口了，但在浦口校区后山大家还都有印象吧，可你知道有一座小山在冬天下大雪的时候是可以滑雪的，SEU很喜欢滑雪，这并不奇怪，因为滑雪的确很刺激，可是为了获得速度，滑雪区域必须向下倾斜，而且当你到底时不得不重新走到上面重滑。SEU想知道在这个区域中最长的滑坡。区域由一个二维数组给出。数组的每一个数字表示山坡上一个点的高度。下面是一个例子：一个人可以从一个点滑向上下左右相邻的四个点之一，当且仅当高度减小。在上面的例子中，一条可行的滑坡为24-17-16-1(从24开始，在1结束)。当然25－24-23-…－3-2-1更长。事实上，这是最长的一条。输入要求：从文件输入，输入的第一行为表示区域的二维数组的R行数和C列数（都在1到100之间）。下面是R行，每行有C个数，代表高度。输出要求：输出数据到文件，输出区域中最长滑坡的长度。输入样例： 5 5 1 2 3 4 5 16 17 18 19 6 15 24 25 20 7 14 23 22 21 8 13 12 11 10 9 输出样例： -Problem Description : Although left Pukou, but in Pukou Campus of the mountain we also have the impression it. You know a hill under the snow in winter is the time to ski, ski SEU like, it is not surprising, because skiing is very stimulating, but in order to obtain the speed, skiing region must downward slopping. but in the end when you had to re-entering the sliding weight above. SEU would like to know the longest in the region of landslide. From the region is a two-dimensional array. Every one of the array of figures on the hillside a high point. Here is an example : a person can be slid from a point adjacent to the next around one of the four points, and only if highly reduced. In the example above, a possible landslide as 24-17-16-1 (from 24, in a conclusion). Of course 25-24-23
Date : 2025-12-18 Size : 1kb User : laochao
[AI-NN-PR] NN+GA DL : 0: 1,改进BP神经网络在股市预测中的应用.2,基于MATLAB工具箱的开采煤层自燃危险性预测.3,基于改进的神经网络的电力系统负荷预报.4,基于神经网络的灌溉用水量预测.5,基于遗传算法改进BP网络的地表沉陷预计.6,利用遗传算法改进BP学习算法.7,模糊神经网络在电力市场短期负荷预测中的应用.8,神经网络学习算法存在的问题及对策.9,遗传神经网络在电力系统短期负荷预测中的应用.10,应用改进BP神经网络进行用水量预测.11,用遗传算法改进的BP模型在刹车系统诊断中的应用研究.12,遗传算法改进的BP神经网络对汛期三门峡水库泥沙冲淤量的计算13,基于遗传算法的人工神经网络学习算法14.自适应遗传算法优化管网状态估计神经网络模型.15,基于GA_RBF神经网络的电梯交通流模式识别的研究-Improved BP Neural Network in Stock Market Prediction
Date : 2025-12-18 Size : 721kb User : 军军
[AI-NN-PR] Simple Demo DL : 0: 人搜索依赖在python3.7 MacOS 10.14.6下测试 ——python包 ——opencv-python ——tb-nightly -torch>= 1.0 下载weights 从这里下载，密码:qscx 下载后，将权重放到文件夹 person_search_demo/weights中测试 cd <指向此floder>的路径 python search 结果将保存在输出文件夹中训练您可以直接使用原始的YOLO代码进行培训。行人再识别模型采用强里德基线模型。(Person search Dependencies Tested under python3.7 MacOS 10.14.6 - python packages - opencv-python - tb-nightly - torch >= 1.0 Download weights Download from here, password:qscx After download, put the weights into the floder person_search_demo/weights Test cd <path to this floder> python search.py The results will be saved in the output folder Train You can directly use the original YOLO code for training. Pedestrian re-identification model is adopted strong Reid baseline model.)
Date : 2025-12-18 Size : 5.56mb User : Kyfafyd