"NCV7344 CAN FD 收发器：高速、低功耗综合文档"

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NCV7344

www.onsemi.com

TYPICAL APPLICATION

Micro−

controller

VBAT

GND

CANH

CANL

CAN

BUS

5V −reg

=60W

GND

STB

RxD

TxD

IN OUT

NCV7344

Figure 3. Application Diagram NCV7344−0

5V −reg

Micro−

controller

VBAT

GND

CANH

CANL

CAN

BUS

3V −reg

=60W

GND

STB

RxD

TxD

NCV7344−3

IN OUT

Figure 4. Application Diagram NCV7344−3

Table 1. PIN FUNCTION DESCRIPTION

Pin Name Description

1 TxD

Transmit data input; low input Ù dominant driver; internal pull−up current

2 GND Ground

3 V

Supply voltage

4 RxD

Receive data output; dominant transmitter Ù low output

Not connected. On NCV7344−0 only

Digital Input / Output pins and other functions supply voltage. On NCV7344−3 only

6 CANL Low−level CAN bus line (low in dominant mode)

7 CANH High−level CAN bus line (high in dominant mode)

8 STB Standby mode control input; internal pull−up current

EP Exposed Pad. Recommended to connect to GND or left floating in application (DFN8 package only).

剩余14页未读，继续阅读

read(911,) Para_Inlst07 IF (TRIM(Para_Inlst07)=='POOL_STRUCTURE') THEN read(911,) NCV_Structure(N3Dpool) ENDIF allocate(OBCV_X(NCV_Structure(N3Dpool))) allocate(OBCV_Y(NCV_Structure(N3Dpool))) allocate(OBCV_Z(NCV_Structure(N3Dpool))) allocate(GAMA_OBCV(NCV_Structure(N3Dpool))) allocate(GAMA_OBX(NCV_Structure(N3Dpool))) allocate(GAMA_OBY(NCV_Structure(N3Dpool))) allocate(GAMA_OBZ(NCV_Structure(N3Dpool)))

这段代码首先从文件911中读取一个字符串Para_Inlst07，然后判断这个字符串是否等于"POOL_STRUCTURE"，如果是，则从文件911中读取一个数值NCV_Structure(N3Dpool)。接着，这段代码调用了allocate函数，分别为OBCV_X、...

NCV1117ST33T3G怎么用

NCV1117ST33T3G是一款低压差稳压器，它可以将输入电压降低到一个稳定的输出电压。使用时，需要将输入电压接到芯片的输入引脚，将输出电压接到芯片的输出引脚，同时将芯片的地引脚接到电路的地线上。需要注意的是，...

read(911,) Para_Inlst07 IF (TRIM(Para_Inlst07)=='3DCV_AQ') THEN read(911,) AQ_Type_Input(N3Dpool) read(911,*) NCV_AQ(N3Dpool) ENDIF

首先读取一个字符串类型的参数 Para_Inlst07，如果该参数的值为 "3DCV_AQ"，则会继续读取两个参数 AQ_Type_Input 和 NCV_AQ，并将它们分别赋值给数组 AQ_Type_Input 和 NCV_AQ 的第 N3Dpool 个元素。这些数组可能会...

class SVDRecommender: def init(self, k=50, ncv=None, tol=0, which='LM', v0=None, maxiter=None, return_singular_vectors=True, solver='arpack'): self.k = k self.ncv = ncv self.tol = tol self.which = which self.v0 = v0 self.maxiter = maxiter self.return_singular_vectors = return_singular_vectors self.solver = solver def svds(self, A): if self.which == 'LM': largest = True elif self.which == 'SM': largest = False else: raise ValueError("which must be either 'LM' or 'SM'.") if not (isinstance(A, LinearOperator) or isspmatrix(A) or is_pydata_spmatrix(A)): A = np.asarray(A) n, m = A.shape if self.k <= 0 or self.k >= min(n, m): raise ValueError("k must be between 1 and min(A.shape), k=%d" % self.k) if isinstance(A, LinearOperator): if n > m: X_dot = A.matvec X_matmat = A.matmat XH_dot = A.rmatvec XH_mat = A.rmatmat else: X_dot = A.rmatvec X_matmat = A.rmatmat XH_dot = A.matvec XH_mat = A.matmat dtype = getattr(A, 'dtype', None) if dtype is None: dtype = A.dot(np.zeros([m, 1])).dtype else: if n > m: X_dot = X_matmat = A.dot XH_dot = XH_mat = _herm(A).dot else: XH_dot = XH_mat = A.dot X_dot = X_matmat = _herm(A).dot def matvec_XH_X(x): return XH_dot(X_dot(x)) def matmat_XH_X(x): return XH_mat(X_matmat(x)) XH_X = LinearOperator(matvec=matvec_XH_X, dtype=A.dtype, matmat=matmat_XH_X, shape=(min(A.shape), min(A.shape))) #获得隐式定义的格拉米矩阵的低秩近似。 eigvals, eigvec = eigsh(XH_X, k=self.k, tol=self.tol ** 2, maxiter=self.maxiter, ncv=self.ncv, which=self.which, v0=self.v0) #格拉米矩阵有实非负特征值。 eigvals = np.maximum(eigvals.real, 0) #使用来自pinvh的小特征值的复数检测。 t = eigvec.dtype.char.lower() factor = {'f': 1E3, 'd': 1E6} cond = factor[t] * np.finfo(t).eps cutoff = cond * np.max(eigvals) #获得一个指示哪些本征对不是简并微小的掩码， #并为阈值奇异值创建一个重新排序数组。 above_cutoff = (eigvals > cutoff) nlarge = above_cutoff.sum() nsmall = self.k - nlarge slarge = np.sqrt(eigvals[above_cutoff]) s = np.zeros_like(eigvals) s[:nlarge] = slarge if not self.return_singular_vectors: return np.sort(s) if n > m: vlarge = eigvec[:, above_cutoff] ularge = X_matmat(vlarge) / slarge if self.return_singular_vectors != 'vh' else None vhlarge = _herm(vlarge) else: ularge = eigvec[:, above_cutoff] vhlarge = _herm(X_matmat(ularge) / slarge) if self.return_singular_vectors != 'u' else None u = _augmented_orthonormal_cols(ularge, nsmall) if ularge is not None else None vh = _augmented_orthonormal_rows(vhlarge, nsmall) if vhlarge is not None else None indexes_sorted = np.argsort(s) s = s[indexes_sorted] if u is not None: u = u[:, indexes_sorted] if vh is not None: vh = vh[indexes_sorted] return u, s, vh def _augmented_orthonormal_cols(U, n): if U.shape[0] <= n: return U Q, R = np.linalg.qr(U) return Q[:, :n] def _augmented_orthonormal_rows(V, n): if V.shape[1] <= n: return V Q, R = np.linalg.qr(V.T) return Q[:, :n].T def _herm(x): return np.conjugate(x.T) 将上述代码修改为使用LM，迭代器使用arpack

将代码修改为使用LM和迭代器使用arpack的方法如下： ```python class SVDRecommender: def __init__(self, k=50, ncv=None, tol=0, which='LM', v0=None, maxiter=None, return_singular_vectors=True, solver='...

物理：已知1mol的某种实际气体,在一定温度下,其压强随体积变化的函数关系为p=c/(v-b)-a/(v)^2式中a,b,c均为常量.求该气体系统经一准静态过程,体积由V1膨胀到V2时,气体对外界所做的功.

根据热力学第一定律，气体对...∆W=nCv(T2-T1)=nCv^((pV2)/(nR)-pV1)/(nR))(T2-T1) 其中，n为1mol，Cv可以看成是常量，T1和T2为气体的初始温度和末温度，p和V1、V2为已知数据，可以代入计算得到气体对外界所做的功。

class SVDRecommender: def init(self, k=50, ncv=None, tol=0, which='LM', v0=None, maxiter=None, return_singular_vectors=True, solver='arpack'): self.k = k self.ncv = ncv self.tol = tol self.which = which self.v0 = v0 self.maxiter = maxiter self.return_singular_vectors = return_singular_vectors self.solver = solver def svds(self, A): if which == 'LM': largest = True elif which == 'SM': largest = False else: raise ValueError("which must be either 'LM' or 'SM'.") if not (isinstance(A, LinearOperator) or isspmatrix(A) or is_pydata_spmatrix(A)): A = np.asarray(A) n, m = A.shape if k <= 0 or k >= min(n, m): raise ValueError("k must be between 1 and min(A.shape), k=%d" % k) if isinstance(A, LinearOperator): if n > m: X_dot = A.matvec X_matmat = A.matmat XH_dot = A.rmatvec XH_mat = A.rmatmat else: X_dot = A.rmatvec X_matmat = A.rmatmat XH_dot = A.matvec XH_mat = A.matmat dtype = getattr(A, 'dtype', None) if dtype is None: dtype = A.dot(np.zeros([m, 1])).dtype else: if n > m: X_dot = X_matmat = A.dot XH_dot = XH_mat = _herm(A).dot else: XH_dot = XH_mat = A.dot X_dot = X_matmat = _herm(A).dot def matvec_XH_X(x): return XH_dot(X_dot(x)) def matmat_XH_X(x): return XH_mat(X_matmat(x)) XH_X = LinearOperator(matvec=matvec_XH_X, dtype=A.dtype, matmat=matmat_XH_X, shape=(min(A.shape), min(A.shape))) # Get a low rank approximation of the implicitly defined gramian matrix. eigvals, eigvec = eigsh(XH_X, k=k, tol=tol ** 2, maxiter=maxiter, ncv=ncv, which=which, v0=v0) # Gramian matrix has real non-negative eigenvalues. eigvals = np.maximum(eigvals.real, 0) # Use complex detection of small eigenvalues from pinvh. t = eigvec.dtype.char.lower() factor = {'f': 1E3, 'd': 1E6} cond = factor[t] * np.finfo(t).eps cutoff = cond * np.max(eigvals) # Get a mask indicating which eigenpairs are not degenerate tiny, # and create a reordering array for thresholded singular values. above_cutoff = (eigvals > cutoff) nlarge = above_cutoff.sum() nsmall = k - nlarge slarge = np.sqrt(eigvals[above_cutoff]) s = np.zeros_like(eigvals) s[:nlarge] = slarge if not return_singular_vectors: return np.sort(s) if n > m: vlarge = eigvec[:, above_cutoff] ularge = X_matmat(vlarge) / slarge if return_singular_vectors != 'vh' else None vhlarge = _herm(vlarge) else: ularge = eigvec[:, above_cutoff] vhlarge = _herm(X_matmat(ularge) / slarge) if return_singular_vectors != 'u' else None u = _augmented_orthonormal_cols(ularge, nsmall) if ularge is not None else None vh = _augmented_orthonormal_rows(vhlarge, nsmall) if vhlarge is not None else None indexes_sorted = np.argsort(s) s = s[indexes_sorted] if u is not None: u = u[:, indexes_sorted] if vh is not None: vh = vh[indexes_sorted] return u, s, vh将这段代码放入一个.py文件中，用Spyder查看，有报错，可能是缩进有问题，无法被调用，根据这个问题，给出解决办法，给出改正后的完整代码

eigvals, eigvec = eigsh(XH_X, k=self.k, tol=self.tol ** 2, maxiter=self.maxiter, ncv=self.ncv, which=self.which, v0=self.v0) # Gramian matrix has real non-negative eigenvalues. eigvals = np....

do ii=1,CircuitN_v do while(Logi(ii)==.false.) CondiPump0_v=CondiPump_v Dfr_max=0.0 Dp_max=0.0 CALL CalBCV(ii) CALL CalALS(ii) CALL CalIMP(ii) CALL CalArrayCD(ii) Dpressure_v(ii,1:NCV_v(ii))=Gauss(ArrayC_v(ii,1:NCV_v(ii),1:NCV_v(ii)),ArrayD_v(ii,1:NCV_v(ii)),NCV_v(ii)) CALL CalDflowRate(ii) do i=1,NCV_v(ii) if(Dp_max<abs(Dpressure_v(ii,i))) then Dp_max=abs(Dpressure_v(ii,i)) end if end do do i=1,NLS_v(ii) if(Dfr_max<abs(DflowRate_v(ii,i))) then Dfr_max=abs(DflowRate_v(ii,i)) end if end do if(Dfr_max>1000.0) then !CFR600!20180404wangjin修改 DT_v=DT_v0.75 !20200322 open(unit=36,file='输出卡_错误.dat') !20200322 write(36,) TimeNow_v,"Dfr_max=",Dfr_max,"DT_v=",DT_v !if(DT_v<1e-5) then ! DT_v=1e-5 !end if cycle else Logi(ii)=.true. end if do i=1,NCV_v(ii) CV_v(ii,i)%pressure=CV_v(ii,i)%pressure+Dpressure_v(ii,i) end do do i=1,NLS_v(ii) LS_v(ii,i)%flowRate=LS_v(ii,i)%flowRate+DflowRate_v(ii,i) !20190318 调试 !if(LS_v(ii,i)%flowRate<0.0) LS_v(ii,i)%flowRate=0.1 end do CondiPump_v=CondiPump0_v end do end do

这段代码是一个嵌套的循环结构，其中包含了多个子过程的调用。每个子过程的功能在代码中没有给出，你需要根据实际需求自行添加或者修改。在这段代码中，有两个主要的循环： 1. 外层循环 `do ii=1,CircuitN_v`，...

class svd_recommender_py(): #svd矩阵推荐 def svds(A, ncv=None, tol=0, which='LM', v0=None, maxiter=None, return_singular_vectors=True, solver='arpack'): if which == 'LM': largest = True elif which == 'SM': largest = False else: raise ValueError("which must be either 'LM' or 'SM'.") if not (isinstance(A, LinearOperator) or isspmatrix(A) or is_pydata_spmatrix(A)): A = np.asarray(A) n, m = A.shape if k <= 0 or k >= min(n, m): raise ValueError("k must be between 1 and min(A.shape), k=%d" % k) if isinstance(A, LinearOperator): if n > m: X_dot = A.matvec X_matmat = A.matmat XH_dot = A.rmatvec XH_mat = A.rmatmat else: X_dot = A.rmatvec X_matmat = A.rmatmat XH_dot = A.matvec XH_mat = A.matmat dtype = getattr(A, 'dtype', None) if dtype is None: dtype = A.dot(np.zeros([m, 1])).dtype else: if n > m: X_dot = X_matmat = A.dot XH_dot = XH_mat = _herm(A).dot else: XH_dot = XH_mat = A.dot X_dot = X_matmat = _herm(A).dot def matvec_XH_X(x): return XH_dot(X_dot(x)) def matmat_XH_X(x): return XH_mat(X_matmat(x)) XH_X = LinearOperator(matvec=matvec_XH_X, dtype=A.dtype, matmat=matmat_XH_X, shape=(min(A.shape), min(A.shape))) # Get a low rank approximation of the implicitly defined gramian matrix. #获得隐式定义的格拉米矩阵的低秩近似。 #这不是解决问题的稳定方法。 solver == 'arpack' eigvals, eigvec = eigsh(XH_X, k=k, tol=tol ** 2, maxiter=maxiter, ncv=ncv, which=which, v0=v0) #格拉米矩阵具有实非负特征值。 eigvals = np.maximum(eigvals.real, 0) #使用来自pinvh的小特征值的复杂检测。 t = eigvec.dtype.char.lower() factor = {'f': 1E3, 'd': 1E6} cond = factor[t] * np.finfo(t).eps cutoff = cond * np.max(eigvals) #得到一个指示哪些本征对不是退化微小的掩码， #并创建阈值奇异值的重新排序数组。 above_cutoff = (eigvals > cutoff) nlarge = above_cutoff.sum() nsmall = k - nlarge slarge = np.sqrt(eigvals[above_cutoff]) s = np.zeros_like(eigvals) s[:nlarge] = slarge if not return_singular_vectors: return np.sort(s) if n > m: vlarge = eigvec[:, above_cutoff] ularge = X_matmat(vlarge) / slarge if return_singular_vectors != 'vh' else None vhlarge = _herm(vlarge) else: ularge = eigvec[:, above_cutoff] vhlarge = _herm(X_matmat(ularge) / slarge) if return_singular_vectors != 'u' else None u = _augmented_orthonormal_cols(ularge, nsmall) if ularge is not None else None vh = _augmented_orthonormal_rows(vhlarge, nsmall) if vhlarge is not None else None indexes_sorted = np.argsort(s) s = s[indexes_sorted] if u is not None: u = u[:, indexes_sorted] if vh is not None: vh = vh[indexes_sorted] return u, s, vh这段代码主要是为了将scipy包中的SVD计算方法封装成一个自定义类，是否封装合适？如果不合适，给出修改后的完整代码

eigvals, eigvec = eigsh(XH_X, k=k, tol=tol ** 2, maxiter=maxiter, ncv=ncv, which=which, v0=v0) # Gramian matrix has real non-negative eigenvalues. eigvals = np.maximum(eigvals.real, 0) # Use ...

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Exception caught in handler {"worker_id": "0", "exc": "OpenCV(4.8.0) /io/opencv/modules/imgproc/src/contours.cpp:195: error: (-210:Unsupported format or combination of formats) [Start]FindContours supports only CV_8UC1 images when mode != CV_RETR_FLOODFILL otherwise supports CV_32SC1 images only in function 'cvStartFindContours_Impl'\n", "traceback": "Traceback (most recent call last):\n File \"/opt/nuclio/_nuclio_wrapper.py\", line 118, in serve_requests\n await self._handle_event(event)\n File \"/opt/nuclio/_nuclio_wrapper.py\", line 312, in _handle_event\n entrypoint_output = self._entrypoint(self._context, event)\n File \"/opt/nuclio/main.py\", line 51, in handler\n contours, _ = cv2.findContours(mask_binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\ncv2.error: OpenCV(4.8.0) /io/opencv/modules/imgproc/src/contours.cpp:195: error: (-210:Unsupported format or combination of formats) [Start]FindContours supports only CV_8UC1 images when mode != CV_RETR_FLOODFILL otherwise supports CV_32SC1 images only in function 'cvStartFindContours_Impl'\n\n"} 2023-07-16 10:23:22 /opt/nuclio/_nuclio_wrapper.py:395: DeprecationWarning: There is no current event loop 2023-07-16 10:23:22 loop = asyncio.get_event_loop() 2023-07-16 10:24:38 2023-07-16 10:24:40 0: 480x640 1 car, 1 truck, 82.9ms 2023-07-16 10:24:40 Speed: 16.6ms preprocess, 82.9ms inference, 24.2ms postprocess per image at shape (1, 3, 480, 640)

根据你提供的错误信息，问题出现在 `cv2.findContours()` 函数调用时。错误信息提示到了组合格式不受支持的问题。这个问题通常是由于输入图像的格式不正确导致的。`cv2.findContours()` 函数需要传入一个二值图像...

Do i=1, NCV_Structure(N3Dpool) read(911,*)CV_Structure_x,CV_Structure_y,CV_Structure_z,Gama_CV,Gama_X,Gama_Y,Gama_z OBCV_X(i) =CV_Structure_x OBCV_Y(i) =CV_Structure_y OBCV_Z(i) =CV_Structure_z GAMA_OBCV(i) =Gama_CV GAMA_OBX(i) =Gama_X GAMA_OBY(i) =Gama_Y GAMA_OBZ(i) =Gama_z OBCV_X_Cal(N3Dpool,i) =OBCV_X(i) OBCV_Y_Cal(N3Dpool,i) =OBCV_Y(i) OBCV_Z_Cal(N3Dpool,i) =OBCV_Z(i) GAMA_OBCV_Cal(N3Dpool,i) =GAMA_OBCV(i) GAMA_OBX_Cal(N3Dpool,i) =GAMA_OBX(i) GAMA_OBY_Cal(N3Dpool,i) =GAMA_OBY(i) GAMA_OBZ_Cal(N3Dpool,i) =GAMA_OBZ(i) ENDDO

这段代码使用了一个Do循环，循环的次数是NCV_Structure(N3Dpool)。在循环的每一次，从文件911中读取了7个数值，分别是CV_Structure_x、CV_Structure_y、CV_Structure_z、Gama_CV、Gama_X、Gama_Y和Gama_z。然后将...

1、2 mol双原子理想气体从始态300K，50 dm3 ，先恒容加热至 400 K，再恒压加热至体积增大至 100m3，求整个过程的Q，W，△U，△H及△S。

U2 = nCvΔT + nRΔT = 1.205 mol x 20.785 J/(mol*K) x 1200 K + 1.205 mol x 8.314 J/(mol*K) x 1200 K = 34717.43 J 根据焓的定义，ΔH = ΔU + PΔV，其中ΔV为气体体积的变化，由于恒压加热过程中气体体积增大...

from scipy.sparse.linalg import eigsh, LinearOperator from scipy.sparse import isspmatrix, is_pydata_spmatrix class SVDRecommender: def init(self, k=50, ncv=None, tol=0, which='LM', v0=None, maxiter=None, return_singular_vectors=True, solver='arpack'): self.k = k self.ncv = ncv self.tol = tol self.which = which self.v0 = v0 self.maxiter = maxiter self.return_singular_vectors = return_singular_vectors self.solver = solver def svds(self, A): if self.which == 'LM': largest = True elif self.which == 'SM': largest = False else: raise ValueError("which must be either 'LM' or 'SM'.") if not (isinstance(A, LinearOperator) or isspmatrix(A) or is_pydata_spmatrix(A)): A = np.asarray(A) n, m = A.shape if self.k <= 0 or self.k >= min(n, m): raise ValueError("k must be between 1 and min(A.shape), k=%d" % self.k) if isinstance(A, LinearOperator): if n > m: X_dot = A.matvec X_matmat = A.matmat XH_dot = A.rmatvec XH_mat = A.rmatmat else: X_dot = A.rmatvec X_matmat = A.rmatmat XH_dot = A.matvec XH_mat = A.matmat dtype = getattr(A, 'dtype', None) if dtype is None: dtype = A.dot(np.zeros([m, 1])).dtype else: if n > m: X_dot = X_matmat = A.dot XH_dot = XH_mat = _herm(A).dot else: XH_dot = XH_mat = A.dot X_dot = X_matmat = _herm(A).dot def matvec_XH_X(x): return XH_dot(X_dot(x)) def matmat_XH_X(x): return XH_mat(X_matmat(x)) XH_X = LinearOperator(matvec=matvec_XH_X, dtype=A.dtype, matmat=matmat_XH_X, shape=(min(A.shape), min(A.shape))) #获得隐式定义的格拉米矩阵的低秩近似。 eigvals, eigvec = eigsh(XH_X, k=self.k, tol=self.tol ** 2, maxiter=self.maxiter, ncv=self.ncv, which=self.which, v0=self.v0) #格拉米矩阵有实非负特征值。 eigvals = np.maximum(eigvals.real, 0) #使用来自pinvh的小特征值的复数检测。 t = eigvec.dtype.char.lower() factor = {'f': 1E3, 'd': 1E6} cond = factor[t] * np.finfo(t).eps cutoff = cond * np.max(eigvals) #获得一个指示哪些本征对不是简并微小的掩码， #并为阈值奇异值创建一个重新排序数组。 above_cutoff = (eigvals > cutoff) nlarge = above_cutoff.sum() nsmall = self.k - nlarge slarge = np.sqrt(eigvals[above_cutoff]) s = np.zeros_like(eigvals) s[:nlarge] = slarge if not self.return_singular_vectors: return np.sort(s) if n > m: vlarge = eigvec[:, above_cutoff] ularge = X_matmat(vlarge) / slarge if self.return_singular_vectors != 'vh' else None vhlarge = _herm(vlarge) else: ularge = eigvec[:, above_cutoff] vhlarge = _herm(X_matmat(ularge) / slarge) if self.return_singular_vectors != 'u' else None u = _augmented_orthonormal_cols(ularge, nsmall) if ularge is not None else None vh = _augmented_orthonormal_rows(vhlarge, nsmall) if vhlarge is not None else None indexes_sorted = np.argsort(s) s = s[indexes_sorted] if u is not None: u = u[:, indexes_sorted] if vh is not None: vh = vh[indexes_sorted] return u, s, vh def _augmented_orthonormal_cols(U, n): if U.shape[0] <= n: return U Q, R = np.linalg.qr(U) return Q[:, :n] def _augmented_orthonormal_rows(V, n): if V.shape[1] <= n: return V Q, R = np.linalg.qr(V.T) return Q[:, :n].T def _herm(x): return np.conjugate(x.T)这段代码在largest = False处报错了，报错信息为：Local variable 'largest' is assigned to but never used (pyfLakes E)如何改正

这个错误是因为在代码中设置了一个变量 `largest`，但是在后面的代码中没有使用到它。为了避免这个错误，可以将 `largest` 变量的定义和使用放在一起，例如可以将下面这段代码： ``` if self.which == 'LM': ...

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